{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CIFAR10 Image Classification using Keras 简洁版"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import cifar10\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential, load_model, Model\n",
    "from keras.layers import Input, Dense, Dropout, Activation, Flatten\n",
    "from keras.layers import Convolution2D, MaxPooling2D\n",
    "from keras.layers.noise import GaussianNoise\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from time import time\n",
    "from keras.utils import to_categorical\n",
    "from utils import make_parallel, accuracy_curve\n",
    "%matplotlib inline\n",
    "\n",
    "nb_classes = 10\n",
    "class_name = {\n",
    "    0: 'airplane',\n",
    "    1: 'automobile',\n",
    "    2: 'bird',\n",
    "    3: 'cat',\n",
    "    4: 'deer',\n",
    "    5: 'dog',\n",
    "    6: 'frog',\n",
    "    7: 'horse',\n",
    "    8: 'ship',\n",
    "    9: 'truck',\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据预处理：Mean Subtraction Per Channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def mean_subtraction(data):\n",
    "    [mean_r, mean_g, mean_b] = np.mean(data, axis=(0,1,2))\n",
    "    data[:, :, :, 0] -= mean_r\n",
    "    data[:, :, :, 1] -= mean_g\n",
    "    data[:, :, :, 2] -= mean_b\n",
    "    \n",
    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()\n",
    "y_train = y_train.reshape(y_train.shape[0])\n",
    "y_test = y_test.reshape(y_test.shape[0])\n",
    "y_train = to_categorical(y_train, nb_classes)\n",
    "y_test = to_categorical(y_test, nb_classes)\n",
    "\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "mean_subtraction(X_train)\n",
    "mean_subtraction(X_test)\n",
    "X_train /= 255\n",
    "X_test /= 255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建模型：基于VGG16简化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def create_model(n_gpu=1):\n",
    "    \n",
    "    x = Input(shape=(32, 32, 3))\n",
    "    y = x\n",
    "    y = Convolution2D(filters=64, kernel_size=3, strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(y)\n",
    "    y = Convolution2D(filters=64, kernel_size=3, strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(y)\n",
    "    y = MaxPooling2D(pool_size=2, strides=2, padding='same')(y)\n",
    "\n",
    "    y = Convolution2D(filters=128, kernel_size=3, strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(y)\n",
    "    y = Convolution2D(filters=128, kernel_size=3, strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(y)\n",
    "    y = MaxPooling2D(pool_size=2, strides=2, padding='same')(y)\n",
    "\n",
    "    y = Convolution2D(filters=256, kernel_size=3, strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(y)\n",
    "    y = Convolution2D(filters=256, kernel_size=3, strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(y)\n",
    "    y = MaxPooling2D(pool_size=2, strides=2, padding='same')(y)\n",
    "\n",
    "    y = Flatten()(y)\n",
    "    y = Dropout(0.5)(y)\n",
    "    y = Dense(units=nb_classes, activation='softmax', kernel_initializer='he_normal')(y)\n",
    "\n",
    "    model = Model(inputs=x, outputs=y)\n",
    "\n",
    "    model.summary()\n",
    "\n",
    "    if n_gpu > 1:\n",
    "        model = make_parallel(model, n_gpu)\n",
    "\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义训练过程\n",
    "\n",
    "1. **可以选择是否使用数据增强**。\n",
    "2. **使用Early Stopping回调**：监测指标为 val_acc（注：实际上用 val_loss 更好，因为可以在过拟合前及时停止训练）\n",
    "3. **使用自动保存性能提升模型的回调**：只记录性能得到提升的训练代模型。每次训练都将训练代模型放在一个文件夹内，方便管理\n",
    "4. **使用Tensoboard回调**：可以自动在Tensorboard上对性能指标绘图，方便后期对比模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from datetime import datetime\n",
    "\n",
    "def train(model, batch, epoch, data_augmentation=True):\n",
    "    start = time()\n",
    "    log_dir = datetime.now().strftime('model_%Y%m%d_%H%M')\n",
    "    os.mkdir(log_dir)\n",
    "    \n",
    "    es = EarlyStopping(monitor='val_acc', patience=20)\n",
    "    mc = ModelCheckpoint(log_dir + '\\\\CIFAR10-EP{epoch:02d}-ACC{val_acc:.4f}.h5', \n",
    "                         monitor='val_acc', save_best_only=True)\n",
    "    tb = TensorBoard(log_dir=log_dir, histogram_freq=0)\n",
    "    \n",
    "    if data_augmentation:\n",
    "        aug = ImageDataGenerator(width_shift_range = 0.125, height_shift_range = 0.125, horizontal_flip = True)\n",
    "        aug.fit(X_train)\n",
    "        gen = aug.flow(X_train, y_train, batch_size=batch)\n",
    "        h = model.fit_generator(generator=gen, \n",
    "                                 steps_per_epoch=50000/batch, \n",
    "                                 epochs=epoch, \n",
    "                                 validation_data=(X_test, y_test),\n",
    "                                 callbacks=[es, mc, tb])\n",
    "    else:\n",
    "        start = time()\n",
    "        h = model.fit(x=X_train, \n",
    "                      y=y_train, \n",
    "                      batch_size=batch, \n",
    "                      epochs=epoch, \n",
    "                      validation_data=(X_test, y_test),\n",
    "                      callbacks=[es, mc, tb])\n",
    "    \n",
    "    print('\\n@ Total Time Spent: %.2f seconds' % (time() - start))\n",
    "    acc, val_acc = h.history['acc'], h.history['val_acc']\n",
    "    m_acc, m_val_acc = np.argmax(acc), np.argmax(val_acc)\n",
    "    print(\"@ Best Training Accuracy: %.2f %% achieved at EP #%d.\" % (acc[m_acc] * 100, m_acc + 1))\n",
    "    print(\"@ Best Testing Accuracy: %.2f %% achieved at EP #%d.\" % (val_acc[m_val_acc] * 100, m_val_acc + 1))\n",
    "    return h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_4 (InputLayer)         (None, 32, 32, 3)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_19 (Conv2D)           (None, 32, 32, 64)        1792      \n",
      "_________________________________________________________________\n",
      "conv2d_20 (Conv2D)           (None, 32, 32, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_10 (MaxPooling (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_21 (Conv2D)           (None, 16, 16, 128)       73856     \n",
      "_________________________________________________________________\n",
      "conv2d_22 (Conv2D)           (None, 16, 16, 128)       147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_11 (MaxPooling (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_23 (Conv2D)           (None, 8, 8, 256)         295168    \n",
      "_________________________________________________________________\n",
      "conv2d_24 (Conv2D)           (None, 8, 8, 256)         590080    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_12 (MaxPooling (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                40970     \n",
      "=================================================================\n",
      "Total params: 1,186,378\n",
      "Trainable params: 1,186,378\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model1 = create_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练模型：Batch Size = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "782/781 [==============================] - 18s - loss: 1.6196 - acc: 0.4136 - val_loss: 1.2328 - val_acc: 0.5514\n",
      "Epoch 2/200\n",
      "782/781 [==============================] - 17s - loss: 1.1634 - acc: 0.5847 - val_loss: 1.1631 - val_acc: 0.6021\n",
      "Epoch 3/200\n",
      "782/781 [==============================] - 18s - loss: 0.9466 - acc: 0.6673 - val_loss: 0.7904 - val_acc: 0.7256\n",
      "Epoch 4/200\n",
      "782/781 [==============================] - 18s - loss: 0.8163 - acc: 0.7162 - val_loss: 0.8244 - val_acc: 0.7137\n",
      "Epoch 5/200\n",
      "782/781 [==============================] - 18s - loss: 0.7205 - acc: 0.7520 - val_loss: 0.6976 - val_acc: 0.7654\n",
      "Epoch 6/200\n",
      "782/781 [==============================] - 18s - loss: 0.6574 - acc: 0.7725 - val_loss: 0.6195 - val_acc: 0.7880\n",
      "Epoch 7/200\n",
      "782/781 [==============================] - 18s - loss: 0.5986 - acc: 0.7920 - val_loss: 0.7246 - val_acc: 0.7607\n",
      "Epoch 8/200\n",
      "782/781 [==============================] - 17s - loss: 0.5590 - acc: 0.8075 - val_loss: 0.6350 - val_acc: 0.7864\n",
      "Epoch 9/200\n",
      "782/781 [==============================] - 17s - loss: 0.5188 - acc: 0.8209 - val_loss: 0.5715 - val_acc: 0.8074\n",
      "Epoch 10/200\n",
      "782/781 [==============================] - 17s - loss: 0.4839 - acc: 0.8307 - val_loss: 0.4607 - val_acc: 0.8446\n",
      "Epoch 11/200\n",
      "782/781 [==============================] - 17s - loss: 0.4554 - acc: 0.8417 - val_loss: 0.4513 - val_acc: 0.8460\n",
      "Epoch 12/200\n",
      "782/781 [==============================] - 17s - loss: 0.4308 - acc: 0.8513 - val_loss: 0.6757 - val_acc: 0.7832\n",
      "Epoch 13/200\n",
      "782/781 [==============================] - 17s - loss: 0.4102 - acc: 0.8589 - val_loss: 0.4876 - val_acc: 0.8393\n",
      "Epoch 14/200\n",
      "782/781 [==============================] - 17s - loss: 0.3846 - acc: 0.8673 - val_loss: 0.6535 - val_acc: 0.7942\n",
      "Epoch 15/200\n",
      "782/781 [==============================] - 17s - loss: 0.3649 - acc: 0.8758 - val_loss: 0.4373 - val_acc: 0.8545\n",
      "Epoch 16/200\n",
      "782/781 [==============================] - 17s - loss: 0.3506 - acc: 0.8779 - val_loss: 0.4143 - val_acc: 0.8645\n",
      "Epoch 17/200\n",
      "782/781 [==============================] - 17s - loss: 0.3309 - acc: 0.8841 - val_loss: 0.5307 - val_acc: 0.8378\n",
      "Epoch 18/200\n",
      "782/781 [==============================] - 17s - loss: 0.3184 - acc: 0.8896 - val_loss: 0.4115 - val_acc: 0.8642\n",
      "Epoch 19/200\n",
      "782/781 [==============================] - 17s - loss: 0.3038 - acc: 0.8949 - val_loss: 0.4149 - val_acc: 0.8654\n",
      "Epoch 20/200\n",
      "782/781 [==============================] - 17s - loss: 0.2930 - acc: 0.8972 - val_loss: 0.4420 - val_acc: 0.8600\n",
      "Epoch 21/200\n",
      "782/781 [==============================] - 17s - loss: 0.2810 - acc: 0.9028 - val_loss: 0.5191 - val_acc: 0.8423\n",
      "Epoch 22/200\n",
      "782/781 [==============================] - 17s - loss: 0.2713 - acc: 0.9064 - val_loss: 0.4577 - val_acc: 0.8636\n",
      "Epoch 23/200\n",
      "782/781 [==============================] - 17s - loss: 0.2575 - acc: 0.9091 - val_loss: 0.3939 - val_acc: 0.8799\n",
      "Epoch 24/200\n",
      "782/781 [==============================] - 17s - loss: 0.2502 - acc: 0.9131 - val_loss: 0.4468 - val_acc: 0.8633\n",
      "Epoch 25/200\n",
      "782/781 [==============================] - 17s - loss: 0.2407 - acc: 0.9155 - val_loss: 0.4034 - val_acc: 0.8718\n",
      "Epoch 26/200\n",
      "782/781 [==============================] - 17s - loss: 0.2322 - acc: 0.9196 - val_loss: 0.4518 - val_acc: 0.8612\n",
      "Epoch 27/200\n",
      "782/781 [==============================] - 18s - loss: 0.2227 - acc: 0.9233 - val_loss: 0.4348 - val_acc: 0.8711\n",
      "Epoch 28/200\n",
      "782/781 [==============================] - 18s - loss: 0.2167 - acc: 0.9250 - val_loss: 0.4435 - val_acc: 0.8713\n",
      "Epoch 29/200\n",
      "782/781 [==============================] - 18s - loss: 0.2073 - acc: 0.9285 - val_loss: 0.4699 - val_acc: 0.8629\n",
      "Epoch 30/200\n",
      "782/781 [==============================] - 18s - loss: 0.2013 - acc: 0.9297 - val_loss: 0.4084 - val_acc: 0.8775\n",
      "Epoch 31/200\n",
      "782/781 [==============================] - 18s - loss: 0.1915 - acc: 0.9335 - val_loss: 0.4429 - val_acc: 0.8687\n",
      "Epoch 32/200\n",
      "782/781 [==============================] - 18s - loss: 0.1875 - acc: 0.9354 - val_loss: 0.4307 - val_acc: 0.8786\n",
      "Epoch 33/200\n",
      "782/781 [==============================] - 18s - loss: 0.1848 - acc: 0.9354 - val_loss: 0.4775 - val_acc: 0.8708\n",
      "Epoch 34/200\n",
      "782/781 [==============================] - 18s - loss: 0.1778 - acc: 0.9375 - val_loss: 0.4339 - val_acc: 0.8786\n",
      "Epoch 35/200\n",
      "782/781 [==============================] - 18s - loss: 0.1711 - acc: 0.9403 - val_loss: 0.4678 - val_acc: 0.8692\n",
      "Epoch 36/200\n",
      "782/781 [==============================] - 18s - loss: 0.1653 - acc: 0.9420 - val_loss: 0.4269 - val_acc: 0.8867\n",
      "Epoch 37/200\n",
      "782/781 [==============================] - 18s - loss: 0.1626 - acc: 0.9434 - val_loss: 0.4553 - val_acc: 0.8734\n",
      "Epoch 38/200\n",
      "782/781 [==============================] - 18s - loss: 0.1598 - acc: 0.9440 - val_loss: 0.4022 - val_acc: 0.8852\n",
      "Epoch 39/200\n",
      "782/781 [==============================] - 18s - loss: 0.1530 - acc: 0.9465 - val_loss: 0.4343 - val_acc: 0.8808\n",
      "Epoch 40/200\n",
      "782/781 [==============================] - 18s - loss: 0.1502 - acc: 0.9479 - val_loss: 0.4402 - val_acc: 0.8767\n",
      "Epoch 41/200\n",
      "782/781 [==============================] - 18s - loss: 0.1492 - acc: 0.9483 - val_loss: 0.4460 - val_acc: 0.8921\n",
      "Epoch 42/200\n",
      "782/781 [==============================] - 18s - loss: 0.1420 - acc: 0.9503 - val_loss: 0.5512 - val_acc: 0.8701\n",
      "Epoch 43/200\n",
      "782/781 [==============================] - 18s - loss: 0.1386 - acc: 0.9521 - val_loss: 0.4277 - val_acc: 0.8835\n",
      "Epoch 44/200\n",
      "782/781 [==============================] - 18s - loss: 0.1364 - acc: 0.9530 - val_loss: 0.4349 - val_acc: 0.8837\n",
      "Epoch 45/200\n",
      "782/781 [==============================] - 18s - loss: 0.1355 - acc: 0.9532 - val_loss: 0.4693 - val_acc: 0.8766\n",
      "Epoch 46/200\n",
      "782/781 [==============================] - 18s - loss: 0.1296 - acc: 0.9554 - val_loss: 0.4750 - val_acc: 0.8862\n",
      "Epoch 47/200\n",
      "782/781 [==============================] - 18s - loss: 0.1262 - acc: 0.9562 - val_loss: 0.4739 - val_acc: 0.8897\n",
      "Epoch 48/200\n",
      "782/781 [==============================] - 18s - loss: 0.1233 - acc: 0.9578 - val_loss: 0.4587 - val_acc: 0.8907\n",
      "Epoch 49/200\n",
      "782/781 [==============================] - 18s - loss: 0.1227 - acc: 0.9564 - val_loss: 0.4527 - val_acc: 0.8894\n",
      "Epoch 50/200\n",
      "782/781 [==============================] - 18s - loss: 0.1168 - acc: 0.9596 - val_loss: 0.5027 - val_acc: 0.8811\n",
      "Epoch 51/200\n",
      "782/781 [==============================] - 18s - loss: 0.1126 - acc: 0.9608 - val_loss: 0.4638 - val_acc: 0.8890\n",
      "Epoch 52/200\n",
      "782/781 [==============================] - 18s - loss: 0.1162 - acc: 0.9599 - val_loss: 0.5242 - val_acc: 0.8828\n",
      "Epoch 53/200\n",
      "782/781 [==============================] - 18s - loss: 0.1130 - acc: 0.9605 - val_loss: 0.4300 - val_acc: 0.8916\n",
      "Epoch 54/200\n",
      "782/781 [==============================] - 18s - loss: 0.1096 - acc: 0.9620 - val_loss: 0.4999 - val_acc: 0.8869\n",
      "Epoch 55/200\n",
      "782/781 [==============================] - 18s - loss: 0.1057 - acc: 0.9639 - val_loss: 0.4513 - val_acc: 0.8911\n",
      "Epoch 56/200\n",
      "782/781 [==============================] - 18s - loss: 0.1100 - acc: 0.9623 - val_loss: 0.4861 - val_acc: 0.8887\n",
      "Epoch 57/200\n",
      "782/781 [==============================] - 18s - loss: 0.1072 - acc: 0.9624 - val_loss: 0.4982 - val_acc: 0.8836\n",
      "Epoch 58/200\n",
      "782/781 [==============================] - 18s - loss: 0.0997 - acc: 0.9650 - val_loss: 0.4786 - val_acc: 0.8922\n",
      "Epoch 59/200\n",
      "782/781 [==============================] - 18s - loss: 0.1020 - acc: 0.9651 - val_loss: 0.4952 - val_acc: 0.8930\n",
      "Epoch 60/200\n",
      "782/781 [==============================] - 18s - loss: 0.0982 - acc: 0.9667 - val_loss: 0.4940 - val_acc: 0.8945\n",
      "Epoch 61/200\n",
      "782/781 [==============================] - 18s - loss: 0.0955 - acc: 0.9669 - val_loss: 0.5898 - val_acc: 0.8678\n",
      "Epoch 62/200\n",
      "782/781 [==============================] - 18s - loss: 0.0961 - acc: 0.9675 - val_loss: 0.5885 - val_acc: 0.8726\n",
      "Epoch 63/200\n",
      "782/781 [==============================] - 18s - loss: 0.0951 - acc: 0.9677 - val_loss: 0.5115 - val_acc: 0.8924\n",
      "Epoch 64/200\n",
      "782/781 [==============================] - 18s - loss: 0.0930 - acc: 0.9681 - val_loss: 0.4872 - val_acc: 0.8891\n",
      "Epoch 65/200\n",
      "782/781 [==============================] - 18s - loss: 0.0928 - acc: 0.9682 - val_loss: 0.4994 - val_acc: 0.8876\n",
      "Epoch 66/200\n",
      "782/781 [==============================] - 18s - loss: 0.0897 - acc: 0.9689 - val_loss: 0.5383 - val_acc: 0.8787\n",
      "Epoch 67/200\n",
      "782/781 [==============================] - 18s - loss: 0.0902 - acc: 0.9694 - val_loss: 0.4747 - val_acc: 0.8973\n",
      "Epoch 68/200\n",
      "782/781 [==============================] - 17s - loss: 0.0871 - acc: 0.9698 - val_loss: 0.5068 - val_acc: 0.8937\n",
      "Epoch 69/200\n",
      "782/781 [==============================] - 16s - loss: 0.0872 - acc: 0.9705 - val_loss: 0.5365 - val_acc: 0.8864\n",
      "Epoch 70/200\n",
      "782/781 [==============================] - 16s - loss: 0.0849 - acc: 0.9704 - val_loss: 0.5783 - val_acc: 0.8804\n",
      "Epoch 71/200\n",
      "782/781 [==============================] - 16s - loss: 0.0856 - acc: 0.9707 - val_loss: 0.5432 - val_acc: 0.8939\n",
      "Epoch 72/200\n",
      "782/781 [==============================] - 16s - loss: 0.0843 - acc: 0.9714 - val_loss: 0.5259 - val_acc: 0.8938\n",
      "Epoch 73/200\n",
      "782/781 [==============================] - 16s - loss: 0.0871 - acc: 0.9701 - val_loss: 0.4898 - val_acc: 0.8955\n",
      "Epoch 74/200\n",
      "782/781 [==============================] - 16s - loss: 0.0839 - acc: 0.9718 - val_loss: 0.5000 - val_acc: 0.8935\n",
      "Epoch 75/200\n",
      "782/781 [==============================] - 16s - loss: 0.0803 - acc: 0.9730 - val_loss: 0.5110 - val_acc: 0.8932\n",
      "Epoch 76/200\n",
      "782/781 [==============================] - 16s - loss: 0.0796 - acc: 0.9735 - val_loss: 0.4975 - val_acc: 0.8887\n",
      "Epoch 77/200\n",
      "782/781 [==============================] - 16s - loss: 0.0819 - acc: 0.9721 - val_loss: 0.4806 - val_acc: 0.8872\n",
      "Epoch 78/200\n",
      "782/781 [==============================] - 16s - loss: 0.0771 - acc: 0.9740 - val_loss: 0.5435 - val_acc: 0.8904\n",
      "Epoch 79/200\n",
      "782/781 [==============================] - 16s - loss: 0.0786 - acc: 0.9735 - val_loss: 0.5510 - val_acc: 0.8958\n",
      "Epoch 80/200\n",
      "782/781 [==============================] - 16s - loss: 0.0776 - acc: 0.9743 - val_loss: 0.5281 - val_acc: 0.8899\n",
      "Epoch 81/200\n",
      "782/781 [==============================] - 16s - loss: 0.0768 - acc: 0.9735 - val_loss: 0.5412 - val_acc: 0.8934\n",
      "Epoch 82/200\n",
      "782/781 [==============================] - 16s - loss: 0.0789 - acc: 0.9739 - val_loss: 0.5648 - val_acc: 0.8919\n",
      "Epoch 83/200\n",
      "782/781 [==============================] - 16s - loss: 0.0740 - acc: 0.9756 - val_loss: 0.5228 - val_acc: 0.8886\n",
      "Epoch 84/200\n",
      "782/781 [==============================] - 16s - loss: 0.0721 - acc: 0.9759 - val_loss: 0.5331 - val_acc: 0.8954\n",
      "Epoch 85/200\n",
      "782/781 [==============================] - 16s - loss: 0.0707 - acc: 0.9763 - val_loss: 0.5237 - val_acc: 0.9000\n",
      "Epoch 86/200\n",
      "782/781 [==============================] - 16s - loss: 0.0740 - acc: 0.9757 - val_loss: 0.5353 - val_acc: 0.8975\n",
      "Epoch 87/200\n",
      "782/781 [==============================] - 16s - loss: 0.0728 - acc: 0.9762 - val_loss: 0.5982 - val_acc: 0.8961\n",
      "Epoch 88/200\n",
      "782/781 [==============================] - 16s - loss: 0.0755 - acc: 0.9752 - val_loss: 0.4894 - val_acc: 0.8938\n",
      "Epoch 89/200\n",
      "782/781 [==============================] - 16s - loss: 0.0730 - acc: 0.9756 - val_loss: 0.5457 - val_acc: 0.8957\n",
      "Epoch 90/200\n",
      "782/781 [==============================] - 16s - loss: 0.0676 - acc: 0.9775 - val_loss: 0.5149 - val_acc: 0.8972\n",
      "Epoch 91/200\n",
      "782/781 [==============================] - 16s - loss: 0.0728 - acc: 0.9769 - val_loss: 0.5647 - val_acc: 0.8907\n",
      "Epoch 92/200\n",
      "782/781 [==============================] - 16s - loss: 0.0701 - acc: 0.9770 - val_loss: 0.5579 - val_acc: 0.8936\n",
      "Epoch 93/200\n",
      "782/781 [==============================] - 16s - loss: 0.0712 - acc: 0.9758 - val_loss: 0.5929 - val_acc: 0.8948\n",
      "Epoch 94/200\n",
      "782/781 [==============================] - 16s - loss: 0.0686 - acc: 0.9772 - val_loss: 0.5943 - val_acc: 0.8957\n",
      "Epoch 95/200\n",
      "782/781 [==============================] - 16s - loss: 0.0676 - acc: 0.9773 - val_loss: 0.5659 - val_acc: 0.8857\n",
      "Epoch 96/200\n",
      "782/781 [==============================] - 16s - loss: 0.0692 - acc: 0.9779 - val_loss: 0.5072 - val_acc: 0.8903\n",
      "Epoch 97/200\n",
      "782/781 [==============================] - 16s - loss: 0.0689 - acc: 0.9779 - val_loss: 0.5729 - val_acc: 0.8888\n",
      "Epoch 98/200\n",
      "782/781 [==============================] - 16s - loss: 0.0643 - acc: 0.9789 - val_loss: 0.6303 - val_acc: 0.8949\n",
      "Epoch 99/200\n",
      "782/781 [==============================] - 16s - loss: 0.0682 - acc: 0.9772 - val_loss: 0.6486 - val_acc: 0.8932\n",
      "Epoch 100/200\n",
      "782/781 [==============================] - 16s - loss: 0.0679 - acc: 0.9780 - val_loss: 0.5434 - val_acc: 0.8949\n",
      "Epoch 101/200\n",
      "782/781 [==============================] - 16s - loss: 0.0670 - acc: 0.9779 - val_loss: 0.6773 - val_acc: 0.8849\n",
      "Epoch 102/200\n",
      "782/781 [==============================] - 16s - loss: 0.0685 - acc: 0.9779 - val_loss: 0.5419 - val_acc: 0.8942\n",
      "Epoch 103/200\n",
      "782/781 [==============================] - 16s - loss: 0.0658 - acc: 0.9787 - val_loss: 0.6280 - val_acc: 0.8937\n",
      "Epoch 104/200\n",
      "782/781 [==============================] - 16s - loss: 0.0632 - acc: 0.9788 - val_loss: 0.6060 - val_acc: 0.8946\n",
      "Epoch 105/200\n",
      "782/781 [==============================] - 16s - loss: 0.0656 - acc: 0.9782 - val_loss: 0.5996 - val_acc: 0.8947\n",
      "Epoch 106/200\n",
      "782/781 [==============================] - 16s - loss: 0.0661 - acc: 0.9782 - val_loss: 0.6633 - val_acc: 0.8938\n",
      "\n",
      "@ Total Time Spent: 1875.35 seconds\n",
      "@ Best Training Accuracy: 97.89 % achieved at EP #98.\n",
      "@ Best Testing Accuracy: 90.00 % achieved at EP #85.\n"
     ]
    },
    {
     "data": {
      "image/png": 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dsEBE7sAOZnMndkCYxwGMMetF5AnsKKg9sOWYAlxkjLksiHwCICKPY5+2L8IO7DINGOKc\nXyml1KGJ8rse+fuUIH7rReRubDPkbGf70dgZQm519rflN/zPwDLgDRF5FNsv+D7soKhf+xIZY2pE\n5DXstbIX8PMGx/kH9h7kYxH5X+ygYb4ZTL4wxsxtvmgaE5EJ2MHXfM3TT3Yegm8zxvgeTD+GvU6/\nJiL3YWdxuQs7gGyxk/dq5z7sfrHTlH2LHal9Co1nWAmkm4gcH2D76gaVA9eJSDV2UNnrsK3pLnfy\n4HXudx4XkXzsPdzJ2Hum25wm/GBnIfkW+ExE/o6t+T4ayDfGzAkir4jIGGwrhxex95ap2O/A98aY\nguZeq1SLQjkqmy5H7oINXjc0s//f2Ce90c56P+yP2gHsKOHfA5f5pY/F/vDlYJuSbwX+4rc/AXgW\ne5HMBW7H/uAGGr08IUB+fosN9suAD7EX10Ajov8UO2hWlXOel4CkBmmmO6+dHmRZxWED1L3OcZcA\npzeR9gvn2D9tYv+V2MFWKpyyXAzc7Lf/GWBJkPnqT9Mjh/f3SyfYhyg5znk/B44OcLzB2IcVZU7e\nnsFvBPAm8jC1mTx86JduG/B37A3CXuccc4GUBscbiJ1SrQTb3/ttYEiDNC5sX74t2FYLOcAcv/3Z\n2JqJQPkc7fdd+9L5PpYDK4Brw/3/UhdddNGlqy/O73xT14WpTppmf+uxXbE+wjbtrgTWYwNu3+wj\nbfoNx9b4LnaOmYe91wl0z+G7T9gFRATY3xvbPc13X7ANeB4Y5ezv77z+nCDL7JkmyuuZBulGYlt8\nVWCbcd+D32jsfuluxt6HVWNrli8IIg/ZzXxuk/3K3WAHTfvSKceNwIUBjvdLbOuDaud6/f8CpBmD\nve8ocZbFwKnOvnrX7Qb5fMX5dw/stHJbnLzkYu8t+ob7/4EuXX/x/dgopdpIRO7HNnMaaOwgHKqd\nicg27EXylnDnRSmllFKtJyJXYx82JJr6s5EoddjR5uVKtZEzCulIbBOnuzXgVkoppZRSSjWkQbdS\nbfc4MBE76uhDYc6LUkoppZRSqhPS5uVKKaWUUkoppVQ70SnDlFJKKaWUUkqpdtJi0C0ic0QkT0RW\nNbFfROQhEdkkIitEZHzos6mUUkoppZRSSnU9wfTpfgZ4GHiuif1nYqdbGoLt3/qo87dZ6enppn//\n/kFlMhhlZWXEx8eH7HiHKy2n4GlZBU/LKjhaTsHrrGW1dOnS/caY7uHOx+EolPcFnfX70xlpWQVP\nyyo4Wk7B07IKXmctq2DvC1oMuo0xn4lI/2aSnA88Z2zn8EUikiIivYwxe5o7bv/+/VmyZElLpw9a\ndnY2U6dODdnxDldaTsHTsgqellVwtJyC11nLSkS2hzsPh6tQ3hd01u9PZ6RlFTwtq+BoOQVPyyp4\nnbWsgr0vCEWf7kxgp996jrNNKaWUUkoppZQ6onXolGEiMhuYDZCRkUF2dnbIjl1aWhrS4x2utJyC\np2UVPC2r4Gg5BU/LSimllFKHi1AE3buAPn7rWc62RowxTwBPAEyYMMGEsolAZ21y0NloOQVPyyp4\nWlbB0XIKnpaVUkoppQ4XoQi65wM3iMg87ABqRS31525KTU0NOTk5VFZWtvq1ycnJrF27ti2nDauY\nmBiysrKIjIwMd1aUUkoppZRSqlmHErO1VbhjvUON2VoMukVkLjAVSBeRHOBOIBLAGPMY8C5wFrAJ\nKAeuaVNOgJycHBITE+nfvz8i0qrXlpSUkJiY2NZTh4Uxhvz8fHJychgwYEC4s6OUUkoppZRSzTqU\nmK2twhnrhSJmC2b08stb2G+AX7Tp7A1UVlZ26IcXbiJCWloa+/btC3dWlFJKKaWUUqpFGrO1XihG\nLw+pI+XD8znS3q9SSimllFKqazvSYphDfb+dLugOp/z8fMaNG8e4cePo2bMnmZmZB9erq6uDOsY1\n11zD+vXr2zmnSimllFJKKXXk6YoxW4dOGdbZpaWlsXz5cgDuuusuEhISuOWWW+qlMcZgjCEiIvDz\niqeffrrd86mUUkoppZRSR6KuGLNpTXcQNm3axMiRI5k1axajRo1iz549zJ49mwkTJjBq1Cj+9Kc/\nHUw7efJkli9fTm1tLSkpKdx6662MHTuWE044gby8vDC+C6WU6lger2FPUQVLthXw5vJdfLR2L3kl\nbRvp1OM17CupYs3uYj7fuI+P1+3lwzV7Wbg6lw/W7GXxlnzW5Razu7CCA2XVFDhLfmkVZVW1IX5n\nqjNbvrOQT3Nqwp0NpZRSHawzx2xa0x2kdevW8dxzzzFhwgQA7r33Xrp160ZtbS3Tpk3joosuYuTI\nkfVeU1RUxMknn8y9997LzTffzJw5c7j11lvDkX2lVBfh9RpqvF6qa72UV3soraqltLKWqlovybGR\npMZFkhIXhTtCKKmspaiihuLKGmKjXPRKjiEuqvHPekW1h1W7i1i+o5DlOYUUldeQHBdJSmwkybGR\nuCOEWq/BYwwAmSmx9E+LZ0B6PIkxbjbmlbJhbwkbckvIL6um1mOo9Xqp8RiKK2soKq/hQHk1JZW1\niECECK4IobrWS63XNMpPRlI0I3slUesE0vll1RSV12CoS2u8BvdHC3BHRBAhUFbtwRPgWMG4/uRB\n3Hrm8Da9VjUmInOAc4A8Y8zoJtJMBf6Fne1kvzHm5I7K33urcnludTV3dtQJlVJKdRqdNWbrtEH3\n3W+tZs3u4qDTezweXC5Xs2lG9k7iznNHtSk/gwYNOvjhAcydO5ennnqK2tpadu/ezZo1axp9gLGx\nsZx55pkAHHPMMXz++edtOrdSKnyqaj1EuSIaDaBhjCG3uJLdhRUkxtjgNSkmkhqvl7ziSvKKq9hX\nWkVclJuMpGh6JMZQ6zWUVNZQXu2hrKqWPUWVLN9ZyPKdhXy/s5D8suqgA0sRMAGSJsdG0iMxGo/X\nUFHjobzaQ0llDb7DZqbE0iMpmt1FFRSV11BYUYPHa3BH2EDZGKj2eAOeMz7KRUZSDG6X4I6IINIl\nJMZEkpkSS0pcJIkxdu5Kr9fg8Rqi3BH0ToklKzWWzJRYCitqWJFTxKpdRazdU0xMpIus1DjG9Ukh\nJS6KCL8i3rZ9B1l9+lDrMXi8XhJi3PRIjKFHYjRpCdFEuSNwiRwsh+LKGooq7FJZ40GoG/RkdGZS\nUGWqgvYM8DDwXKCdIpIC/Bs4wxizQ0R6dGDeSIxx4zH2/260u/n7AqWUUoemtTFbMA7HmK3TBt2d\nTXx8/MF/b9y4kQcffJBvvvmGlJQUrrzyyoCTw0dFRR38t8vlorZWmzgq1dG8XkNRRQ3x0W6i3BGN\n9m3ZX8aaPcXsLChnR345OwrKySuppNipRa6u9ZIQ7WZg93gGdU+gZ3IMG/eW8n1OIftKqlqfofff\nb7RpYHo8kwen0yslhkhXhLMI8dFuEpwlyh1BcUUtBeXVFJZVU+PxkuTUVCfFRlJebYP43CIb8Ltd\nQmyki7goF8lxURyVmczYPsn0SIypd25jTL0HCsYY8kqq2LKvjG35ZZRU1jCkRyJDMhLITIk95NE7\nj+3fLah02dm5TJ064pDOpdqHMeYzEenfTJIrgNeMMTuc9B3atyo+ygbapZW1RCdo0K2UUkeSzhqz\nddqgu7VPNzpywvTi4mISExNJSkpiz549LFy4kDPOOKNDzq3U4coYc7A5dVlVLWVVHsqqa6mo8VBR\nbWtsD5RVk1dSSV5JFftLq/B4Da4IIULs4mvyXOPxUl7lIb+smgPl1QfT9e0Wx6Du8WSmxLJ5Xxnf\n5xRSUln3w9o9MZq+3eIY1jPxYM11QrSb/aVVbN5XxuIt+eQWV9I/PZ6TBqczJiuZfunxlFXVHqxl\ndUcIGUkx9EiMoXtiFGVVHvJKqthbXMl3q9czYshg4qJdxEe5SUuIYkxmCslxkWEr94ZBtIjNf0ZS\nDCcMSgtTrlQXNxSIFJFsIBF40BgTsFa8PcRH21ubsioPaQkddVallDoytbVGuiN0ppit0wbdndn4\n8eMZOXIkw4cPp1+/fpx44onhzpJSHcYYQ2WNl8oaD5W1HiprvGwt8uDauI/Cctu/uKrGS7XH6/y1\naaqctNW1XjxeGxjXeLwcKK9hf2kV+0qqqKoN3KzZX7Q7gh5J0XRPiMYdEUGNxx7PGIPbFYE7QkiI\ndpOeEM34fimkxUeTGh9FUXk1m/aVsjmvjK8359M/PZ5zx/ZmXJ8URvdOpn96XMD+0A15vYaIiLbV\n9mZVbmXqlIFteq1SXYgbOAY4FYgFvhaRRcaYDQ0TishsYDZARkYG2dnZh3zybbn2QVr2l1/TN0lr\nultSWloaknI/EmhZBUfLKXhdtaySk5MpKSnp0HN6PJ6A56yqqiIyMpKSkhJKS0vxer0H0w0ZMoQh\nQ4YwdOhQ+vbty8SJE6moqKCkpASPx0NZWdnBtL6/FRUV1NTUBDxXZWVlmz8vMYE6BXaACRMmmCVL\nltTbtnbtWkaMaFtzwo6s6Q61Q3nfrZWdnc3UqVM75Fxd3ZFaViWVNSzZdoCSqlpqam1gXFxZw6a8\nUjbmlbJpbyklrRgN2h0hxES6iHZHEO2OINJtA+NIVwRul5ASG0V6QhTdE6PpFh9NYoybeKcmOD7a\nTWyUbSIdF+kmOS6SpBj3ITdxDpcj9TvVFp21rERkqTFmQsspD29O8/K3Aw2kJiK3ArHGmDud9aeA\n94wxLzd3zED3BW3x2YZ9XDXnG16+/oSguzMcyTrr/7XOSMsqOFpOweuqZdWRsYtPZ4j1Ar3vYO8L\ntKZbqcNUrcfLutwSlu0sZNmOA2zPL6dftziGZCQyNCOB3imxiIAgeLyG73Yc4IM1e/l6c37AgbTS\nE6IZ0iOBmeMz6ZUcS0xkBDGRLmIiI9i2cR2TjxtPitO/OMbtIsodYQe7amOtsFKqy3oTeFhE3EAU\nMBH4Z0edPCHG3tqU6lRxSimlOgkNupXqxDxew97iSnIOVLCzoJz9pVWkxkXRPSmaHonRJES7Kams\npdjpT7yjoJwNe+30ThvzSqisscFzekIUA9MT+GpzPq8t29Xk+fqlxXHVCf04ZUQPeiTGEOWKINIt\nxEW5SY5tut9xdtEmrVFS6gghInOBqUC6iOQAd2KnBsMY85gxZq2IvAesALzAk8aYVR2Vv4SDfbo1\n6FZKKdU5aNCtVBgVVdSQX1pFn25xRLrqRtZetauIF7/dyRvLd9Ub6CsYGUnRDM1IZNbEfozJSmZ8\n31SyUutGnS6qqGFTXgl5xVUHZ0U2BoZkJDCkR0KXbbqtlOoYxpjLg0jzAPBAB2SnEd9AaqWt/O1U\nSiml2osG3Uq1s6LyGnYVVpBfVkV+aTW5xZWs3l3MypxCtuWXAxDlimBg93iGZiSyZX8pq3YVE+2O\n4MzRPTl2QDf6pMaRlRpL98RoCstr7AjexVWUVXtIinGT5Iy03TslhpS4qGbzkxwbyTH9tFZaKXV4\nSojS5uVKKaU6Fw26lTpEXq+hpLKWqloPVbV2lO7N++wI2Yu25LMut/Hoh5kpsRyVmczFE/rQIzGa\nTftK2ZBbwtLtB0iNj+RP54/i/LGZAaeSSoyJpE+3uI54a0op1eXER9sRy8uqPGHOiVJKKWVp0K1U\nkA6UVbN6dzGrdhexdk8xuwsr2FNka5wDDTwW7Y5gQv9UbpkxlEHdE0hLiCYtIYr0hOhm+0crpZRq\nO7crgsgIKKvWmm6llFKdgwbdfvLz8zn11FMByM3NxeVy0b17dwC++eYboqKab7brM2fOHM466yx6\n9uzZbnlV7S+/wsurS3NYtCWfRVvz2VlQcXBfZkosWamxTOiXSkZyDN0TouumxYp00TMphrF9kol2\n6xyxSinV0WLd2rxcKaUOV10xZtOg209aWhrLly8H4K677iIhIYFbbrml1ceZM2cO48eP16C7E6v1\nePk+p4h1ucVs3FvKprxSdhSUU17toarGQ2WthxqPAb4nJS6SiQO6MWtiP47KTGZU76QW+00rpZQK\nnxi36EBqSil1mOqKMZsG3UF69tlneeSRR6iurmbSpEk8/PDDeL1errnmGpYvX44xhtmzZ5ORkcHy\n5cu59NJLiY2NbdXTFtU+jDFU1ngpKK9m6fYDfLR2L9nr91FUUQNAXJSLIT0SGNcnhfho98H5p4v3\n7uTKGRMO4FvDAAAgAElEQVQZlpFIhM41rZRSXUaMS3TKMKWUOgJ11phNg+4grFq1itdff52vvvoK\nt9vN7NmzmTdvHoMGDWL//v2sXLkSgMLCQlJSUvjf//1fHn74YcaNGxfmnB9ZisprWJtbzPrcEtbv\nLWF9bgk7C8oprKihurauz3W3+Cimj8jglOE9GNc3hV5JMQGD6uzsXEb0SurIt6CUUioEYrR5uVJK\nHXE6c8zWeYPuBbdC7sqgk8d6asHVwtvpeRSceW+rs/Lhhx/y7bffMmHCBAAqKiro06cPp59+OuvX\nr+fGG2/k7LPPZsaMGa0+tmq79bklLFi1h5U5zsBmRZUH9yXHRjKsZyJTh3UnNT6KlNgoZ1sC4/qk\n4tKaa6WUOmzFuEUHUlNKqY7QypgtKIdhzNZ5g+5OxBjDj3/8Y+65555G+1asWMGCBQt45JFHePXV\nV3niiSfCkMMjg9drWL+3hPdX7+XtFbvZmFdKhMDgHgkcO6AbI3olMbxnIiN6JdEjMRoRDayVUupI\nFOuCPJ0yTCmljiidOWbrvEF3K59uVJSUkJiY2C5ZmT59OhdddBG/+tWvSE9PJz8/n7KyMmJjY4mJ\nieHiiy9myJAhXHfddQAkJiZSUtJ4bmbVPI/XsGFvCWt2F+PxGkQgQoSCsmq+2VbAt9sKKCyvQQSO\n7d+Ne84fxRmje9E9MTrcWVdKKdWJxLiF0jKt6VZKqXbXhhrp9tKZY7bOG3R3IkcddRR33nkn06dP\nx+v1EhkZyWOPPYbL5eLaa6/FGIOIcN999wFwzTXXcN111+lAakHYV1LFS0t28vXmfJbvLGyyD16/\ntDhmjMxg4oA0Jg9JJyMppoNzqpRSqquIcaOjlyul1BGmM8dsGnQ34a677qq3fsUVV3DFFVc0Srds\n2bJG2y655BIuueSS9sraYWH17iKe/nIb85fvptrjZVTvJGYencnRfVMYk5VMTKQLY8AYiI1yaW22\nUkqpoMW4hIqaWjxeo2N4KKXUYayrxGwadKt2l1dSSfb6fWzOK2XzvjI27ytl6/4yYiNdXHZcH66e\n1J+B3RPCnU2llFKHiRi3DbTLqmtJiokMc26UUkod6TToVu3CGMOiLQU8v3g7C1flUus1RLki6J8e\nx/Ceicya2JeLj+lDcpzeDCmllAqtWOfupqxKg26llFLhp0G3Cony6lpW7y5m1a4iVu8uZun2A2zd\nX0ZybCQ/mtSfiydkMaRHojbzU0op1e4O1nTrXN1KKaU6gU4XdPs6uB8pjDHhzsIh2ZRXwrNfbefV\n73Ior7bTs6QnRDGqdzI/nzqIc8f2JibSFeZcKqWUOpLEOJedEh1MTSml2oXGbK3TqYLumJgY8vPz\nSUtLOyI+RGMM+fn5xMR0rZG4jTF8tnE/T36+hc837ifKHcF5Y3tz5uiejM5M1jmylVJKhVXswZpu\nnatbKaVCTWO21utUQXdWVhY5OTns27ev1a+trKzscsEr2C9tVlZWuLMRFK/X8P6aXB75ZDMrdxXR\nMymG35w+jMuO7UNago4urpRSqnOIce5umpqGUimlVNsdSszWVuGO9Q41ZutUQXdkZCQDBgxo02uz\ns7M5+uijQ5wj5fEaVuQU8vnG/cz/fjeb8koZkB7P/ReO4QdHZxLljgh3FpVSSql6Ylzap1sppdrL\nocRsbdXVY71OFXSrzmNFTiFPfbGV7PX7KKqoQQTGZqXw0OVHc/ZRvXRANKWUUp1WrN+UYUoppVS4\nadCtDjLGsHhrAY98sonPN+4nKcbNjFE9mTK0O5MHp9MtPircWVRKKaVapM3LlVJKdSYadCu255ex\nYFUu76zYw8pdRaQnRPG7M4Zz5fF9SdT5TZVSSnUxkRHgihBKdfRypZRSnUBQQbeInAE8CLiAJ40x\n9zbYnwrMAQYBlcCPjTGrQpxXFUIFZdW8ujSH15ftYs2eYgCOykzm7vNGcemxfXSaL6WUUgGJyBzg\nHCDPGDO6mXTHAl8DlxljXumo/DnnJj7KpX26lVJKdQotBt0i4gIeAU4DcoBvRWS+MWaNX7LbgOXG\nmJkiMtxJf2p7ZFi1nTGGdQUeXp27jIWrcqn2eBnXJ4Xbzx7B6aN60qdbXLizqJRSqvN7BngYeK6p\nBM69w33A+x2Up0YSot2U6pRhSimlOoFgarqPAzYZY7YAiMg84HzAP+geCdwLYIxZJyL9RSTDGLM3\n1BlWbbOvpIo/vL6S99dUkhSTxxUT+3L5cX0Z1jMx3FlTSinVhRhjPhOR/i0k+yXwKnBsu2eoCQkx\nbq3pVkop1SkEE3RnAjv91nOAiQ3SfA9cAHwuIscB/YAsQIPuTuDtFbv54xurKKv2cMnQSO6+8lRi\no7T5uFJdQsUB+OJfsH4B9DkWhsyAgdMgJincOVMqIBHJBGYC0whj0B0f7dbRy5VSSnUKoRpI7V7g\nQRFZDqwElgGN2nSJyGxgNkBGRgbZ2dkhOj2UlpaG9HhdXXG1Yc1+D4tza1mW52FAcgS3jI8mmXIW\nf/V5uLPXJeh3KnjtUVapBcvokfcFuT1PoSh5JEgXmKbOOD97EvihVmvKKcJTReaud+i741XctWUU\nJY8ifuXrRC57Hq+4KUkcTFl8P8ri+1Ae14eSxMHURiYEPJa7ppiE0u3El20nvmwHlTE92NlnJiai\nlQ/fjOmwz0H//3Vp/wJ+Z4zxSgvfl/a6LygtLaW6zMWuIvR71AL9vxY8LavgaDkFT8sqeF29rMQY\n03wCkROAu4wxpzvrvwcwxvytifQCbAXGGGOKmzruhAkTzJIlS9qa70ays7OZOnVqyI7XFdV4vDz7\n1TbeWL6LVbts0afERfKTkwby0ykDcbsitJxaQcuqgfICyFsDeWshfSgMPPngrpCXldcDjxwH+Zvs\nesZoOPY6GHMpRAUx9kBtFez6DiQCIlw2UDywDXYvg93LYf9GSB8CfY+HPsdD73EQnQiuqLYHlZs+\ngndutseY+RhkHtMoSfYnnzB12rSWj5WzBF6+Gop22prtU++EnqPBUws7F8PGhbDzW9i3DioK7Gsk\nAnqNg0HT7Hs6sA1yvoGd30Dh9rpjRydDVRH0nQQXzYGkXvXPXbzblk/BFrsUbofiPVCyB0py7ed+\n2X/BHR182dRUwlcPQWke9Bpry7v7cHvM3cthz3KoqYCpt0JMsi0r/+/U7mU2L9HJtoY/Mg7K8+3r\ni3fbf3s99qGH1wORsZDYy763xN6QNggSegSf32aIyFJjzISQHKwLc5qXvx1oIDUR2Qr4/iOlA+XA\nbGPMG80dM5T3BdnZ2czbmcjmfaV8cPPJLb/gCKbXuuBpWQVHyyl4WlbB66xlFex9QTA13d8CQ0Rk\nALALuAy4osHJUoByY0w1cB3wWXMBtwq973Yc4LbXVrIut4TxfVO4ZcZQThrSndGZybgiukANYWez\nfxPJhauAqeHOSfBK88AVCbGph3ac2ir49knI3wyle+1xi3baAMcnLh1u2WAD2ubs2wCrX4eR50OP\n4cHnYf27NuD+waPgrYXFT8DbN8GiR2HWS5Dav+nXer3wnwtg+xeN97miIGMUDJwK+9fD5/+oq50G\nQGzAljEKpv4eBp3SchBeug8W3gYrX4K0wVBdBk+eBlN+A1NusbXemz+Cb59iysYPYGVf6DHSniPr\nWNtU3OX3U7z0WXj3Fhs0/ugtGDDFL/9u6H+iXXzK9tuHIdu+hC2f2KbovveU0NM2SZ/wYxu09xgF\niT1h5Svw1q/gsclw4ZO2PFe/Dqtfg9yV9csrpS8k9YZ+J0JkDCx9Bub/EmY+HtwDirx18Oq1sHcV\nRMbDt//nFHUEGK/zbxdgYN96uOKl+uWxfgHMm9Xgc2ogMg4i3HUPWarLobaibv+kG2HGPS3nVYWE\nMWaA798i8gw2OG824G4P8dHap1sppVTn0GLQbYypFZEbgIXYKcPmGGNWi8j1zv7HgBHAsyJigNXA\nte2YZ+WnuLKG+99bxwuLd9AzKYbHf3gMp4/qGe5sdW2eWph3BWMKtsEZV4W272xZPqx729ZCFuXY\npboUug20Na9pQ2yQ1bD20aem0gY+DeWuhGfOsbV8k2+C439eVyOcuxI+e8DWxGaOh8HT7dIjQJPt\n2mp46UewYQHEpUFCBsR3t0Fq9+E2UCzcDu/8GnYsqh/8BbLwNtj0AWT/1QaY46+C4edAXLemX2MM\nfPmgDQTHXGqDqKN/aPP/6rXwf6fC5XOhz3GBX7/sORtwT/uDfb9erw3YknpD9xHgjqpLW1UKu5bA\n3jVQUw61lbbGdc18eP4C6H+SrWXuE6BbauFO+H4eLHrEHufk38Hkm+0xFvwWPr0X1r0DVcW2zOK7\ns6fXaWQmR9ogef27NuhMyICxl8PYy2Dx47D0aRvsX/hU8+XkE59uvzMDpsC030NlEez5HlIHQHJW\n4MB4zMXQawy8dBX85wd127OOhRl/hp5j7HcyqXfjBytJmfDJX+wDhpN/W7d9SzasfBmSsiBjpA3w\nt3wC798OUQk2mB58GhRstrXbeWsgORN6HW2/VytehLduhAW/gbP/YY+57Utb499rLJz3kA2mq4rt\n/5m4NFuLndQLouLr59EYqCx0auh32zyrkBGRudgnkukikgPcCUTCwXuCTiExxk2pBt1KKaU6gaD6\ndBtj3gXebbDtMb9/fw0MDW3WVHOMMby7Mpe73lpNfmkV10wawM0zhpIQHapu+l2Up7Z+LVlbLHsO\n9q/HBbDqVZhwzaEdzxjbJPjbp2DNG+CptrVySZmQ3McGXbkrYO18G4TFpcNVb9qaSf9jfPY/kP03\nOPFXMO02W6sNtrn3c+fbwKPnGPj4HnuuE38F2z63QX5UIow41wZjH9xhl/ShcMa9MNiZ3a+22gY4\nGxbA2X+3zbkDqSqF926DtW81H3Tv32QD7ok/s8HVd/+xNaTzf2mDpYyRNvA/7ie2NtVnxyLI+RbO\n+p+6gE8EhkyH6z6EFy62DxhmPgqjL6x/zpJceP8OGyxP+U3LNbHRCfaBwsCp9befeoet0f3sAXhq\nug1gM0bZ/Cb0sGW65VPA2JrqM+6tq8mPjIELnoBhZ8H7f7TvbfqdMPxcNn7xFZm+plHV5TYo/e4/\n8NX/wpf/stsn3wyn3N5yK4KmxCTXrx1vSvdh8JOP4cuH7AOaUTPrfw5NmfIb29T7k7/UPSz68C7Y\n/LH9nlWXAn7dlgZPh/P/DYkZdj19iF0aOuZHNiD/8kFIG0x8aRzMvcPmadYrEJ8WzLu3RGyLj9hU\n+z1TIWWMubwVaa9ux6w0Kz7aRVm1B2MMLfUtV0oppdrTER6hdU27Ciu4441VfLQuj1G9k3jqRxMY\nk5US7myF36aP4OVr4LIXYMBJjffvWGz/9m04+L6fqhL45K/Q9wRK83eTsOw/hxZ0VxTCfy+xQXd0\nEhxzNYz/EfQY0Tioqq22wfeLP4Rnz4Efvg69j7Y1tQtvg8WP2qDvi3/A1s/goqfsQ4Znz4OISNsU\nOW2QrR384I/w3u9sADb19zDxp3XNzot2waYPbZD3/AU2GJ9+tw3E179jg92mAm6wgergU23Qfcbf\nmg5sv3nC5mvy/7MB1wk32GB6+1e2lnPvGhu4rn4DrnkXUvrY1335oK3FHDer8THTh8B1H8GLs+CV\nH9sa01Nur+tf/N6ttqb5nH8d2oBf7mhbZuNmwXfP2s9vr1/tdEo/2/947GVNN3Uf9QO7NCUqDoaf\nbZfiPbZ5d/pQ+3Cho0TF29rx1hCBcx+Ewh3w+k9t8//YVJjxF/u9MV7b1zxvjW2qP3ImREQEd+xT\n74KCrbDwD4xzJ0BcElz5WusCbqUc8dFuPF5DVa2XmEidsUMppVT4aNDdhRhjeHlJDne9tRpj4Paz\nR3D1pP64XUHe0B7OjIGP/2wHiJr/S/j51/aG32fvanj2XPBU2aa7026HrMYDXfHlg1C2Dy6fR+4n\n/2Xw5qfsazNG1aWpLIb3/2CDV081eGpsM+Az76tfU1hbZfui7lpqA9mxl9uAtSnuKMiaYAPQZ8+D\nZ8+HK160Na4r5tkm4zP+Amteh7dugsdOsn1ZjReuftsG3GBrn6/7yA4klj744MBUByVn2lrFsZfZ\nwa0++7sNoAHOfMDWPLdkxLk2AN29zDbhbqiyCJa/YGuifTWcIrZJuH+z8N3L7Xt97jy4ZoF93YYF\n9kFBUwOmxafZlgALfmvzv/kTuPD/4MB2G7hOu92+71CIToATfmEXsE3Pi3fbmu9gA8lgJPWCE34e\nuuO1N3c0XPo8vPYT27rixF9BrN+Dv8zxgb8XLYmIsH3Fi3fD3vX2wZPvYYxSreRr+VVSWatBt1JK\nqbDSaK2LKKqo4Zdzl/HbV1cwNiuFD26ewnUnDQxtwF1TaQPIrmjzR7D7OxvYHthqm2H71FTAK9fa\n4POUP9om1k+eAnMvt0GjT/Fu+OphGyhmTWBvxlQ7kNR3/6l/ro/uttsqC20fane0rV1+YpptGg22\ndvr1623f4vP/bQPZ5gJuf90G2MA7rhs8fYYNuKfdDqf/1QYloy+E67+wfay9NTYA7T6s/jFE7EOF\nhgG3P3e0bSp8w7cw7ko49yGYODu4PA49ww5+te7twPuX/9c2M5740+aP03scXPkKlOy1TeQ//jO4\nY+HYFgJ/d7Stbb1srh3g7fGT4c1f2DI58VfBvYe2iIy1DzdCGXB3VXHd4MpXbdP52BC2tImKg2sW\nsHjiY42/10q1QnyUDbp1MDWllFLhpjXdnZzHa1i8JZ/fvrqCPUWV/Ob0YVx/8qDQj0juqYUnT7VN\nZS97IbTHbsjrjFgcKHCpKrE1u2Mug4Tujfeve9f2ZR5yWt02Y+DTB+wATuc+ZPd/9TCMusAGdQv/\nAPvW2maqg0+1geDix2w/2iemwqBT7SjTy16wA26degcANVFJtunvinlw2t020NuxyI7sPfFncOa9\ndXnYtwHmXmpr08990NaOr37NNtsee2nryyiljw283/wFjDivcRP31H5w7fu2Nj3QwGqtPdcPHmnd\na+K6Qf/JtobcKa+DvB47IFificHVdvY5ztbov3CRbZZ87E+Cb048/CzbOuDNG+yDl8teqD9Qmuqa\n3FFNzjmuVLDinZpuHUxNKaVUuGnQ3QntLChn/ve7+WZrAd9tP0BJVS1ZqbG8fP0JjO97iNNBNWXF\ni3ZKn72r7Ly+TY0MHQqvXG0H2brqjfpz53pq7UBemz6EJU/bpqWp/er2f/Ev+PBOW8N62Qsw7Ey7\nfdvnsHORbcLtjoLT/gQbFtpm5lNugSVPwaRf1g0YFp1oa3iPm20D6K//DU87x5r0y/p9dI/+oW2y\nvO5tO+r2/Bvt4Gen3F7/PXUfapt0v/wjeONndttxPz20Wtek3rYMmiJy6AH3oRhxrp3aat/6+ts3\nfmBbG5z6x+CPNeAk+5l+ej+ceGPr8pHQwwbtlUWhrXFVSnVpiTFa062UUqpz0DaSnczaPcWc9/AX\nPLBwPbsLKzh3XG/+dek43rtpSvsF3LVVkH2v7ZsZ39028W0vGxbCmjchb7VtTlyWb7cbY6cK2vSh\nHXCrfD88NcPWGHu9dtqhD++0Iyz3GmuDc19T7k/vtyOAH32lXY9NhbMesIOSvXw19BoHp9zROC8x\nyXDSr+GmlXDGfbZG+aRf108zcBok97XNyb/4p53b+Zx/Bm4qHtfN1qZPutHW1jY3yNjhYPg59u/a\n+fW3L37Ujk4+4rzWHW/wdFt7H8wI2g2JaMCtlKrHV9NdVq1Bt1JKqfDSmu5OZM3uYmY9uYiYSBcf\n/XoSg7p3UPPKpc9A0Q4470HIWwcLfw9bP68/AnjhDjt91sSfBa5dPbDdNg32VNtFXDD09PqDmdVW\nwXu/t3NRn3kfzLsC/nM+XDUflj0PS+bYka6n32VHjX7+AlsD3W+yHVX7uNk2OK4ogDmn21HBp99t\na7pn/KX+uUaeb4O+zR/b+Y6ba3IcFQfHX2+XhiIi4OhZto/4ti9g9EX1m7Y35IqEGfc0vf9wktQL\nso6zTcyHHWvHBFj0iJ2v+ZQ/1k1pppRSYZAQbQdPK6nUoFsppVR4adDdSfgH3PNmH0+/tPiOOXF1\nmZ2LuP9Jtla37yQ7IvQnf4H+C2wN4oFtdl7kop227/IP/l2/Bnf3Mnj6LKgpr3/sAVPg8nl2WiKA\nRY/aeXhnvWqbel/2gh3M7MnpdvuomXU10hkj4ccLbeC9/h2Y9oe6eZfj022N8lMz4O2b7PRSgab1\nuuhp2+T4UKcbGjfLtgSITrDzMas6I86FD/5IVtx8ePiX9uHNsLPtAxKllAqjgzXdVZ4w50QppdSR\nTpuXdwKrdxdxxZOLiO3ogBvsgGJl++xgWL4+wlNugR1f24GpCrbagLuqxI5w/f1/7SBZPoU74L+X\nQlw6XPOeHVX7F9/YAc22fQEvXAxVpXYe4s8egGFn1c1DPHg6XPIfKNxua0x/8Gj9wdVS+8F1H8KP\n34eTf1s/0E/tBz98DRJ6OtNLBSgzlzs08/um9LFNxS98KvDgbkeyEbaJ+eDNT0Fssp0r/PL/QkxS\nmDOmlDrS1QXdWtOtlFIqvLSmO8xW7SriyqcWExfpYm5TAXfOUjtNUaA+qzUVtjlv/mYo2GIHsEob\nDNNus32bm1NxwM5LPfTM+gOnHX0VfPEgvH+HrSmuKYMfzYeMo+xrFt4GPUbYkcFfuMQ2K75qPvQY\nXneM7sNsIPzabHj+QkjsaZudn/6X+nkYdgbcsMT2yfZvHu4Tmwp9JwbOf8YouHltx0zfdPzP2v8c\nXVG3gTD9btbmFDDikjshQufCVUp1Dr4pw3T0cqWUUuGmQXcYrdpVxKwnF5MQ7WbuT46nb1pc40Rr\n5sNLP4SxV8DMRxvv/+wB+Pzv9t8xyZDSD7Z8CqvfsFNajbqg6cG8Fj1qg+qGI3G7o2Dq7+x0VbGp\nNqDuNcbum/mYbQ7+8tU28M7faOfq9Q+4fY66yAZhr1xrp+KafLMN0hrqNqDJMmqRzpccfpNvYm92\nNiM04FZKdSKuCCEuyqU13UoppcJOg+4w8Q+4580+nj7dAgTcu5bammLE9mv21NQfnMoYO51V/5Pg\nkufs6NkAe1bAWzfCKz+G7+fB+Y/Un5oL7LGWPgtDZkDP0Y3PPeYy2yR8+Nm2f7VPTBJcPheemAbb\nv4Tz/w0Dpzb9RkfNBFe0nZKs4cjgSimlVDuKj3br6OVKKaXCTqsJw2DJtoKWA+7CHfDfy2ywfO6/\nbI30ts/rp9m72jYpH31BXcANtlb6uo/soF9bP7Mjhje04T0ozYVjAgxABrY/9Mm/qR9w+6QNgqte\nh4vm2JG9WzL8LLjk2cDTbCmllFLtJCHaraOXK6WUCjsNujvY2yt2c8WTi0mLj2o64K4ssoOT1VbB\nFS/BmEshMs5OzeRv7VuA1M2X7C/CZfshHzcbVr9m+3z7W/K0nUt5yIy2vZHMY2D0hW17rVJKKdUB\n4qO1eblSSqnw06C7gxhjeOzTzdzw32WMzUrm1Z9NChxwA3xwJ+xbb2uHewy3A4wNOQ3WvQNeb126\ntfOh36TGTcf9nXADuKLgi3/WbTuwzc5fPf4qW6OtlFJKHYYSot06ZZhSSqmw06C7AxhjuGv+au5d\nsI5zxvTiP9dOJDU+qukX7FoKg6bZxWfEeVC6F3K+tev7N0HeGru9OYkZNrj+fi4U7rTbvnvODq42\n/oeH9saUUkqpTiwh2q2jlyullAo7Dbo7wEMfbeLZr7dz3eQBPHTZ0cRENjPKszG2n3a3QfW3D5lh\na6zXzrfra9+0f0ec23IGJt0ICHz1EOKthWXP2+MlZ7Xp/SillFJdgQ6kppRSqjPQoLs9VBZDzhLI\n38yrS3P454cbuGB8Jn84ewQREU1M3+VTtg+qSxtPrRWTZEcJX/uWDczXzIfMCZCc2XJ+UvrAuMth\n6bP02rPQ1pg3NYCaUkopdZiIj3ZTqgOpKaWUCjPt0BsqB7bBO7fYJt/FuwCojM/idwceYNKgNO69\nYAzS1HzZ/gq22L+B5rMecS5sfN/27d6zHE77U/D5O/EmWPY8QzY+CUmZMHh68K9VSimluiBtXq6U\nUqoz0JruUFn5Cmz6APpPhlPvpHjw+cSU5TA83c2jVx5DlDvIovYF3WmDGu8bdhZIBLx7i10Ppmm5\nT9ogGH0RglcHUFNKKXVEiI9yU1XrpdbjbTmxUkop1U406A6VnYshfRhc8ARFE37Jv3baoPmp87uT\nHBvZOP3Wz8BT03h7/mYQFyT3abwvPh36nQgleyDjqMC14c2Zeiv53Y7RpuVKKaUOa+K119eEGPuA\nWUcwV0opFU4adIeC12uD7r4T8XoNv37pe74vTQUgo2ZP4/R5a+HZc2Hly433FWyxfbDdTYxu7qvd\nHtnCqOWBpA1i5Zg77IjmSiml1OEo+z6O++YX4PWSEG0HLi3VwdSUUkqFkQbdobB/A1QWQZ/jefTT\nzXy4di8XTp9s9x3Y2jh93lr7d/eyxvsCjVzub/RFMPJ8GDfr0POtlFJKHW66DSC2ci/kfEN8tK+m\nW4NupZRS4aNBdyjsXATAUjOUv7+/nvPG9ubyk8dBdJIdYK2h/E32754V9bcfnC6smWbj8WlwyXPB\njVqulFJKhZiIzBGRPBFZ1cT+WSKyQkRWishXIjK2QzM47Ey8EgmrXjsYdJfoCOZKKaXCSIPuUNix\nGE9sGj95+wCDuifwtwuOQiIiILU/FASo6d6/0f7du8o2Tfcpz4eq4tb31VZKKaU6zjPAGc3s3wqc\nbIw5CrgHeKIjMnVQdCL5acfAmjdJiLSzhmhNt1JKqXDSoDsEPNu/ZnHNYGo8hkevPObgk3W6DQjc\nvDzfCbqrS+vvb266MKWUUqoTMMZ8BhQ0s/8rY8wBZ3URkNUhGfOT12MylObS/YDtxqVBt1JKqXDS\noPsQVR7Yg6twK59XDeLxq45hcI+Eup2p/eHAdvD6jZpqDOzfBH0m2vVcvybmzU0XppRSSnU91wIL\nOt7j+fEAACAASURBVPqkBd0mgDuWtG3vAOhc3UoppcJKJ2s+BB6v4dmXXuSnwKRp5zBpUHr9BKkD\nwFsDxbsgpa/dVroXqkvsKOS7lkLuShg10+7L32zn4falVUoppbooEZmGDbonN5NmNjAbICMjg+zs\n7JCcu7TSQ17q0SSve50IprN89Tq6l24OybEPN6WlpSEr98OdllVwtJyCp2UVvK5eVhp0H4J73l5D\nr53f4ImM5KQp0xsn6DbA/i3YWhdI+/pzZ4yy83rvaVDTnZwF7uj2zbhSSinVjkRkDPAkcKYxJr+p\ndMaYJ3D6fE+YMMFMnTo1JOfPzs6mx9Sfwss/YmLEWnr1mcnUqUNCcuzDTXZ2NqEq98OdllVwtJyC\np2UVvK5eVtq8vI0Wrs7lma+2cVbydlxZ4wMHyqlO0O0/grmvP3faEOg1xtZ0+7Q0crlSSinVyYlI\nX+A14IfGmA1hy8iQGRAZz3muRZRWeVpOr5RSSrUTDbrboLiyhjveXMVRGdFkVW6o65/dUHIWRETW\nHyxt/yZwx0JSJvQ8CkpzoTTP7mtpjm6llFIqzERkLvA1MExEckTkWhG5XkSud5LcAaQB/xaR5SKy\nJCwZjYqDYWdwhusbKiorw5IFpZRSCrR5eZvc/9468kqqeP60KOSdauh7fOCEES7brNx/2rD8jZA2\nGCIioOcYuy13BfQeD5WFWtOtlFKqUzPGXN7C/uuA6zooO80bNZPUVa/Sq+BbYFy4c6OUUuoIpTXd\nrbRkWwHPL9rB1ZP6M6Rqld3YVE03OCOY+9d0b4T0wfbfPUfbv3tW6HRhSimlVKgNPo1yYhlZ+Em4\nc6KUUuoIpkF3K1TVerj1tZVkpsRyy4xhsGOxrbWOT2/6Rd0GQME2O1VYbRUUbrf9uQFiU21NeO5K\nnS5MKaWUCrXIGHZGDSSlMifcOVFKKXUE06C7FR7L3sKmvFL+/IPRxEe5YOfi5mu5wQ6mVlUEFQds\nM3PjhXS/EVR7OoOp5W8GBFL6tet7UEoppY4knqgkompLwp0NpZRSR7Cggm4ROUNE1ovIJhG5NcD+\nZBF5S0S+F5HVInJN6LMaXrsLK/h39ibOHtOLacN7wM5voKIA+jc5/ajlmzbswFa/kcsH1+3vOQby\nN9nAOzkLImPa5w0opZRSR6KYZGK9pXi8Jtw5UUopdYRqMegWERfwCHAmMBK4XERGNkj2C2CNMWYs\nMBX4u4hEhTivYfX39zdgDNx6xnC7Yckc+P/s3Xd4VNe19/HvVu+NIgGS6L2DKMYY5A7uJY7BJbZT\ncEty0xynJzc98c0b92DiFleMHeO44LhhuYBteu8dgRBVQr3u948tWV0awUiDRr/P8/AMc86ZM0sb\nEFqz1147JBqGXtH8C+Nr7dV9tLGkeyRgYefimgRdREREvCIwIo5YCjiaX+LrUEREpJPyZKZ7IrDD\nWrvLWlsKzAeurHeNBaKNMQaIAo4D5V6N1Ic2HTzJq6szufXsPqQkREDhcdi4EEZfD6FRzb84vo97\nPLHbzWhHJUFYTM35HlUdzMuLtF2YiIiIl4VExhNNIVk5hb4ORUREOilPku5ewP5azzOrjtX2MDAU\nOAisB/7HWlvplQjPAH96ezMxYcHcnV41Q73mBagogfEeVNGHREBUomumdnR73fXc4PbrDo93v1fn\nchEREa8Ki+lCoLEcOXbM16GIiEgn5a19ui8G1gDnAf2B94wxn1hrT9a+yBgzB5gDkJiYSEZGhpfe\nHvLz8716v2objpbzyfYSZg8JYfWyJWAtE5c9QlnMEFZvOQpbWn7PsQEJ2N1riCzYy5FuU9hWL87R\noSnEF51gw8FCjrbB11BbW42TP9JYeU5j5RmNk+c0VuIt0XFdAMg5dgQY2PzFIiIibcCTpPsAkFLr\neXLVsdpuA/5srbXADmPMbmAIsKz2RdbaecA8gLS0NJuenn6KYTeUkZGBN+8HUFFp+fODn5CSEMBv\nbppOaFAg7PoIPjoIM35N+mgP3+/EGNj8JpTn0XPUdHqeVe91JdPgs3WMmHYFJNZfLu9dbTFO/kpj\n5TmNlWc0Tp7TWIm3RMYkAJCbe9THkYiISGflSXn5cmCgMaZvVXO0WcDr9a7ZB5wPYIxJBAYDu7wZ\nqC+8tvoAWw7l8eOLh7iEG1wDtfB4GFZ/WXsz4vtCadV2JV0a+ZR9xLUw5LK6DdZERETktJnwOAAK\nc4/7OBIREemsWpzpttaWG2O+DbwDBAJPWms3GmPuqDo/F/gd8LQxZj1ggHuttR36I2VrLXM/2snQ\nHjFcNqqHO5iXDVvehEl3QHC45zer3ZW8ayOJda9xMOv50wtYREREGgqLBaA4T0m3iIj4hkdruq21\ni4BF9Y7NrfX7g8BF3g3NB7LWwcu3wM0L+eRIJNsP5/O360bjmrIDq5+BynIYf2vr7lvdwTwwBOJ6\nezNiERERaU5V0l1ecMLHgYiISGflSXl557FsHhzfBRte5YlPd9MtOpTLRlfNclsLq5+DPuc07EDe\nkuq9uhP6QUCgd2MWERGRpoW58vLKohxc6xkREZH2paS7WmmB23sbKN7wOh9tO8LNk3vXrOXOWgMn\n9sCor7b+3pFdISRKa7ZFRETaW2gMAJGVBZwoLPNxMCIi0hl5a8uwjm/T61CaDwMuIGzH+6QE5XDj\npNSa8xtfAxPoGp61ljEw869KukVERNpbYBDlQZHElBeSlVtEQmSIryMSEZFORjPd1dY8D/F9yD3n\n1wD8IGUHXaJC3TlrYdNr0G86RCSc2v3H3gipk7wUrIiIiHiqMjSWGArIPlns61BERKQTUtINrmx8\nzycw5kae2xnOzsoeXBS4suZ81lp3zbCrfBWhiIiInCITHkusKSArV0m3iIi0PyXdAGteBAylI67n\nmc/3sinmHCIPLoGiHHd+02mUlouIiIhPBUXEEWMKOaSkW0REfEBJd2UlrH0B+k3nv5nBZJ8socfk\na93WYDved6XlGxe60vLILr6OVkRERFrJhMeTEFikmW4REfEJJd17P4WcfTDmJp77bC+pCRGMO+tC\niOwOW95UabmIiEhHFxZLnCnUmm4REfEJJd2rn4fQGLbFT2fZnuPcOCmVgMBAGHIJbH8P1i1QabmI\niEhHFhZLFFrTLSIivtG5k+7yUtj8Ogy/mmdXHiYkKIDr0lLcuSGXuS3Els2DvtNUWi4iItJRhcUS\nXllIdm6hryMREZFOqHMn3cd3Qlkhxb3OYuHqA1w2skfN/p19p0FINFSWwXCVlouIiHRYYbEYLKYk\nj7ziMl9HIyIinUznTrqPbAFg8fEE8kvKuXFy75pzQaEw8MKq0vLLfRSgiIjImcUY86Qx5rAxZkMT\n540x5kFjzA5jzDpjzLj2jrGBsFgAYoz26hYRkfbXuZPuw1uwGOZuCGBojxjGpcbVPX/hb+GGBSot\nFxERqfE0MKOZ8zOBgVW/5gD/aIeYmleVdMdSqHXdIiLS7jp30n1kCyXRqazLLuXmyb0xxtQ9H5cC\nAy/wTWwiIiJnIGvtx8DxZi65EnjGOp8DccaYHu0TXRPC3IfqMUbN1EREpP118qR7K9ttMlGhQVw5\npqevoxEREfEHvYD9tZ5nVh3znerycgo5pKRbRETaWZCvA/CZijLssR0sLbuEq8f3IjK08w6FiIiI\nLxhj5uBK0ElMTCQjI8Mr983Pz69zr7CibCYD3YMKWLVlFxmBB7zyPv6g/lhJ0zRWntE4eU5j5bmO\nPladN9M8vgtTWcaWip5cMbS7r6MRERHxFweAlFrPk6uONWCtnQfMA0hLS7Pp6eleCSAjI4M69yrK\ngS8gNaqSA5EJpKdP8Mr7+IMGYyVN0lh5RuPkOY2V5zr6WHXe8vKqzuXbSWZ873gfByMiIuI3Xge+\nVtXFfDKQa63N8mlEoTGAISmkWGu6RUSk3XXeme4jW6nEENx9MDFhwb6ORkREpEMwxrwIpANdjTGZ\nwK+BYABr7VxgEXAJsAMoBG7zTaS1BARAaAxdgoo5dLzI19GIiEgn02mT7srsTRyw3RjdTw3URERE\nPGWtnd3CeQvc3U7heC48loSAQk4UllFcVkFYcKCvIxIRkU6i05aXl2RtZltlLyb0SfB1KCIiItLW\nwmKJMYUAZJ9UibmIiLSfzpl0V5QTkrOT7TaZCX21nltERMTvhcURaQsAtK5bRETaVedMuk/sIdCW\nkRPZj+7RYb6ORkRERNpaWCxh5fkA2qtbRETaVadMuisPbwIgotcwH0ciIiIi7SIsluCyXIyBvccK\nfR2NiIh0Ip0y6T6+Zz0AyYPG+jgSERERaRdhsQSUnKR3QgRbs0/6OhoREelEOmX38rz9Gyi2XRk/\nMNnXoYiIiEh7CIuF0nyGJUeyOSvP19GIiEgn4v8z3Vnr4N1fQkX5l4eCj29lb0AKqQkRPgxMRERE\n2k1YHACjuxn2HCugsLS8hReIiIh4h/8n3Zteg6UPwsd/BcBWlNOtZB9FcYMwxvg4OBEREWkXYbEA\nDE2wWAvbsvN9HJCIiHQW/p90F51wjx/fB3uXcmjvVkIpI7ynmqiJiIh0GlVJ98CYCgC2ZGldt4iI\ntI/OkXTH9IK43vDqHA6szwCg50A1URMREek0qpLuxOBiIkMC2aykW0RE2kknSLpzIKYnXPsE5GUx\nYu3vAUgdrKRbRESk06hKugNKchnSI4bNh9RMTURE2kcnSLpPuOYpyePh3J8RVlnIscBuBIbH+Doy\nERERaS9VSTfFuQxJimZL1kmstb6NSUREOoXOkXSHxwNQNvm7fFg5lv0JZ/k4KBEREWlX4a57OcVu\npvtkcTkHc4t9G5OIiHQKnSDpzvky6d5zvJjbSu9h11l/8nFQIiIi0q5CosAEQHEuQ5OiATVTExGR\n9uFR0m2MmWGM2WqM2WGM+Ukj5+8xxqyp+rXBGFNhjEnwfritVFkBJblffrq9pWr91uCq/2xFRESk\nkzDGlZgX5375c8AWresWEZF20GLSbYwJBB4BZgLDgNnGmDr7bVlr77PWjrHWjgF+CnxkrT3eFgG3\nSnGue6ya6d56KI/AAMOA7lE+DEpERER8oirpjg4LJiUhnE2a6RYRkXbgyUz3RGCHtXaXtbYUmA9c\n2cz1s4EXvRHcaaveo7sq6d5yKI++XSMJDQr0YVAiIiLiE2GxUJwDwJCkGJWXi4hIu/Ak6e4F7K/1\nPLPqWAPGmAhgBvDv0w/NC4rcf6xfznRnn1RpuYiISGdVNdMNMLRHDLuPFlBcVuHjoERExN8Fefl+\nlwNLmiotN8bMAeYAJCYmkpGR4bU3zs/Pb3C/hGOrGAWs2ryLw/s+ZP/xIiZ0Kffq+3Y0jY2TNE5j\n5TmNlWc0Tp7TWEmbCIuFo9sBGJoUTaWF7dn5jEyO9XFgIiLizzxJug8AKbWeJ1cda8wsmiktt9bO\nA+YBpKWl2fT0dM+i9EBGRgYN7rfuCKyHcVPOZ3VRN3h/KTMmjyJ9eJLX3rejaXScpFEaK89prDyj\ncfKcxkraRFjclzPdQ3rEALA566SSbhERaVOelJcvBwYaY/oaY0JwifXr9S8yxsQC04H/eDfE01Br\nTffWqg6lQ5JifBiQiIiI+Eyt8vLUhAjCgwPZfEjrukVEpG21ONNtrS03xnwbeAcIBJ601m40xtxR\ndX5u1aVXA+9aawvaLNrWKq5e0x3HlkOHiQgJJDk+3LcxiYiIiG+ExUFZIZSXEhgUwuCkaHYcPObr\nqERExM95tKbbWrsIWFTv2Nx6z58GnvZWYF5RdAJCoiAwmK2H8hiUGE1AgPF1VCIiIuILYVVl5MW5\nENWNm0I/5vLMv2FzVmHiUn0bm4iI+C1Pyss7rqITEB6PtZat2XkMUedyERGR02KMmWGM2WqM2WGM\n+Ukj52ONMW8YY9YaYzYaY27zRZyNqp10H9/FlVkPEEoZOduW+jYuERHxa36edOdAeBxH8ks4XlCq\n7cJEREROgzEmEHgEmAkMA2YbY4bVu+xuYJO1djSQDvytqieM71Un3UXHYeEdmMAgymwgR3eu8m1c\nIiLi1/w86T4BYXFfNlFT0i0iInJaJgI7rLW7rLWlwHzgynrXWCDaGGOAKOA4UN6+YTYhPM49Lv49\n7P8CLvk/dtELm7XOt3GJiIhf8/+kW53LRUREvKUXsL/W88yqY7U9DAwFDgLrgf+x1la2T3gtqJ7p\n3v0RDLuKoNHXcyRyEPF523wbl4iI+DWPGql1WMU5EB7PlkN5dIsOJSHyzKhuExER8WMXA2uA84D+\nwHvGmE+stQ325jLGzAHmACQmJpKRkeGVAPLz8xu9V0jJMaYAJSHxLI+/lvKPPuJkcE+6Fb7PO2/9\nh9DIzrdfd1NjJQ1prDyjcfKcxspzHX2s/DfptrZqpjuOrXvzGJyo0nIREZHTdABIqfU8uepYbbcB\nf7bWWmCHMWY3MARYVv9m1tp5wDyAtLQ0m56e7pUgMzIyaPRelRWQt4jQtNuY2mcqAFtC8uDdZ4gP\nq2Sil96/I2lyrKQBjZVnNE6e01h5rqOPlf+Wl5cVQkUplWHxbMvO03puERGR07ccGGiM6VvVHG0W\n8Hq9a/YB5wMYYxKBwcCudo2yKQGB8JUnoCrhBug7YhIAObtX+ioqERHxc/470110AoBjFRGUlFcq\n6RYRETlN1tpyY8y3gXeAQOBJa+1GY8wdVefnAr8DnjbGrAcMcK+19qjPgm5BaEx3jgV0JfDwBl+H\nIiIifsqPk+4cADKL3Dpu7dEtIiJy+qy1i4BF9Y7NrfX7g8BF7R3X6TgZO5hex3aQW1RGbHiwr8MR\nERE/47/l5VUz3XsKQwEY2F1Jt4iIiDQU3Gs0/c1BVu485OtQRETED/l90p1ZEkaXyBDCQwJ9HJCI\niIiciboPHE+wqWDPZq3rFhER7/PfpLvYlZfvLwqhe0yYj4MRERGRM1VIrzEA5O1b4+NIRETEH/lv\n0l010727IJikmFAfByMiIiJnrIS+lAWEEZOzmfyScl9HIyIifsa/k+6AIPacDCBRM90iIiLSlIBA\nihKGMIR9rNx7wtfRiIiIn/HrpNuGx3O0oFTl5SIiItKs8JQxDAvYy7JdzexudmQrVFa2X1AiIuIX\n/DjpzqEiJBZrIUlJt4iIiDQjuOcoYk0BO3dsbfyCYzvhkUmw9sX2DUxERDo8P066T1ASHANAotZ0\ni4iISHOSRgJQkbWO7JPFDc/vXQJY2PFe+8YlIiIdnl8n3QUB1Um3ZrpFRESkGd2HYTEMYS8LVx9o\neH7/Mve4+2OVmIuISKv4b9JdnENeQBSgpFtERERaEBqFSejH2VFZ/HtlJtbauuczl0NgCBQeg+wN\nvolRREQ6JP9NuotyyKmMICjA0CUyxNfRiIiIyJkuaSQjAvaw/XA+6zJza44XnYAjW2DsTe757o98\nE5+IiHRI/pl0V5RDyUmOVkTSPTqUgADj64hERETkTNdnKlFFBxgSdIh/r8qsOZ650j0OuxK6DIRd\nSrpFRMRz/pl0F7tPp7PLwrVdmIiIiHhm0AwA7uixjf+sOUhJeYU7nrkMTAD0Gg/9psPepVBe6sNA\nRUSkI/HPpLvoBAAHi0O1XZiIiIh4Ji4FEkdyLivJLSpj8ebD7vj+ZdB9OIRGQ790KCuAAytP/X3y\nD8Om/3gjYhER/1acC6/OIbg0x9eRnBa/Trr3FYdquzARERHx3OAZxBxdyaDoMl5ZmQmVFS7BTpng\nzveZ6ma9d2Wc+nuseBIWfA0Kj3slZBERv7X7Y1j3El2OrfB1JKfFr5PurBKVl4uIiEgrDJqJsZV8\nJ3U3GduOcHzPOig5CckT3fnweOgx+vSaqeVWrRfP2Xf68YqI+LPsTQBEFuw9/XvteB8+vg/Kik7/\nXq3kn0l3sSs/yCVS5eUiIiLiuZ5jISqRdFZSUWnZ+MX77njKxJpr+k53W4iV5J/ae+Qdco9KukVE\nmnd4I+ClpPuLx2DFU277x3bmn0l31Ux3jo3SHt0iIiLiuYAAGHgR0ZkfMSElkoJdn2EjukBCv5pr\n+k2HynLY99mpvUdelntsLOmurITHL4CNC0/t3iIiHU1ZMRSfbPzclzPdp/khZV427PgARl0PAYGn\nd69T4NdJdy6RJMVqTbeIiIi0wuCZUHKSu/sfYWDJJnK6jAVTa/vRlMlupuRU13WfPOgeG0u68w66\nWfQ9n57avUVEOpLMlfDQeHjumobnyorg+E4IiyO09AQUHDv191n/MtgKGD371O9xGvw06c6hNDCK\nCgK1pltERERap186BIYyNe+/9A/IYklxv7rnQyIgZdKp7dddVgxFVQ3UGku6j+9yj9WJuYiIP7IW\nlj8OT15c9WHjii+3ff7Ska1gK2H4Ve754U2n/n5rX3TbPnYbdOr3OA1+mnSfoDAwivDgQKJDg3wd\njYiIiHQkIZHQbzpBG/8NwItZSeQU1tuXu990yF5fsz7bU9Wl5dB40n1sp3s8eaB19xUR6SjKimDh\nHfDWD92HnNc+DliXeNdWnWSP+Erd562VtQ6yN/hslhv8OOnOM9EkxYZhapeDiYiIiHhi8EzAYk0g\nK8v78u9V9ZLgoVe4x/WvtO6+1Ul3lwEu6ba27nnNdIuIv1vyAKybD+k/gxsWwIALAeOW1tSWvRGC\nwqD3FMqCohtPut/9Jbx8K5QWNP1+a+dDQDCMuNabX0Wr+G3SfcJG0j1a67lFRES8yRgzwxiz1Riz\nwxjzkyauSTfGrDHGbDTGnMbeWj40aAYAJmkkQ1ISeXHZPmztBLnbYOg5zpUstkZ10p0yGUrzvuxD\n86XqpLvgCJTXm10XEenoKspg5dMw4AJIv9c1rwyLge7DYP+yutce3uS+1wYEUhDZ+8umanXuteJJ\n13jy2auhKKfx91u/AAZdDBEJbfZltcQ/k+7iHI5VRJAUq/XcIiIi3mKMCQQeAWYCw4DZxphh9a6J\nAx4FrrDWDgeua/dAvSGmpytFHHMDN0xKZcfhfJbvqZcgj7nBlSweWu/5fU9WJd2pk9xj/RLz6qQb\n6paii4j4g61vu+9tad+oezxlgisvr6ysOZa9CboPB6AgMhUOb65bHZS5HErzYezNcGAVPH0Z5B+u\ne9+di92HmGNuaKMvyDN+mXTbohNkl4VruzARERHvmgjssNbustaWAvOBK+tdcwPwqrV2H4C1tt5P\nQB3I1XNh0u1cNqoH0aFBvLisXoI84lpXsrimFbPdeVkQFA5JI93z2kl3ZSUc3+1mfEAl5iLif1Y8\nATHJbua5tuSJUJILR7e654XHIf8QJLrvhwWRqa46KHd/zWt2LgYTCBf9Hm54yXU6f3IG7PzQNa0E\nWPMChCdUlbD7jv8l3dZC0QmOV6q8XERExMt6AbV+4iGz6lhtg4B4Y0yGMWalMeZr7RZdG4kICeKq\nsb14a31W3YZqEQnuB8f1C1wJoydOHoSYHhDX2z2vnXTnH4LyIugztepaNVMTkVaqKHd7Up+Jju10\nWy2Ov7XhXtkpVdU/1SXm2RvdY9WHkPlRfdzzw5trXrNzMSSnQXgcDDgfbn4NCo/Bs1fBX/rAs9e4\nmfWR10FQSBt9UZ7xqLW3MWYG8AAQCDxurf1zI9ekA/cDwcBRa+10L8bpudICTGU5OTaSUSovFxER\naW9BwHjgfCAc+MwY87m1dlv9C40xc4A5AImJiWRkZHglgPz8fK/dq9qgwEpKyyv55fMZXDuw5oe3\nLkGjGFnwJusX3s+xrhNavM+YzC1ABGu+WMvUwHAObVzKjtIRAMSdWM8YYENBPCOAnas/Yf+xrl79\nOupri7HyVxorz2icPNcWY9V7z3xS9i/ks7OepiIo3Kv3jjuxlsiCTILKCwisKKAyIIx9qddQGejZ\nRGf/HU/RywTyefEASut/3dZydlA0R5f/h615vemV+SYDgaU7cynNzKDYuvXYuz57g30HQwkqy+Ps\nA6vY02cWe2vdKzBtLnE5G4g/sYb4rDWEV1awsnIYBT7+O9li0l1r/daFuE+0lxtjXrfWbqp1TfX6\nrRnW2n3GmO5tFXCLqhqS5BKl8nIRERHvOgCk1HqeXHWstkzgmLW2ACgwxnwMjAYaJN3W2nnAPIC0\ntDSbnp7ulSAzMjLw1r1qW1Gwmrc3HOKeayaS2iXCHSyfArseY2TlBki/p+WbrCmA5Amkn3subO5H\ncmQlydWxrtwLa2HEBTfA9kfo3y2c/m3wddTWVmPljzRWHti4kMIvfk5EXFcIiYbQKLeWdlj9VSgC\nbfB3qrICVt0FFcWcMyC6pmrGGza9Dhm/qnkeFAblxfSJKoWvPAkt7RhVVgRf3ApDL2fKxVc3fs3B\nKfQ4sZse6enw+qsQnsCUi64GY9yHEzHJ9IsqoV96umuehqXv+V+nb8rEejeaWfPbinImBPp+C2lP\nyss71vqtqqQ7x0aSGK2kW0RExIuWAwONMX2NMSHALOD1etf8B5hqjAkyxkQAk4DN+IGfXTKU4ADD\nb9+s1UE3KMTtIbt1UcNO5PVZ6/b1junhnsel1i0vP77LrRGPTXGN3FReLh3NqmcJKs+DqCTcvsvL\n4f3fNNwar63VbsbVmez+uOb7Rv09r09HwVF48/uQNAp+tAN+cQR+kQ0X/AY2vgof/1/L99j4mvse\nOeEbTV+TMhGObnPruQ9vgsThdZP57kNryst3LobQWLeLRHPOgIQbPEu6O9b6rar/vLJtAt1jtKZb\nRETEW6y15cC3gXdwifQCa+1GY8wdxpg7qq7ZDPwXWAcswy1L2+CrmL0pMSaM754/kPc3Z/Phllrz\nC2NmQ0UpbHi1+RsUnYCKEoju6Z5XJ93VCcnxnRDfx611jOmp7uXSsVSUw/4vONLtbLhxAdy2CM79\nmfsw6WiDQpe2U5IP/zjLJfudzZoXXCIamwIHvJh0L/oRFOe65pJR3WrWR5/9PRh1PXz4ezcT3pwV\nT0DXQdDnnKavqZ6xzlzukuvuw+qeTxzmGq1VlLlmaf2mnTFJdUu8FaVH67faau0W1KyJSN27PvJ6\nmgAAIABJREFUiH7AgaBefL7kE6/d319onY3nNFae01h5RuPkOY3VmctauwhYVO/Y3HrP7wPua8+4\n2sttZ/flpRX7+d83NjJlQBdCgwKhxxjoNhTWv9z8LE51N/LaM93Ve3VHJLjO5V36V13TyzUcEuko\nstZCaT45ccNrZucGzYS3fugqQboNbp84lj4IR7a4X/3Ph77NJHn+pPgkbH7DfQhYkgd7PvXOfTe8\n6kq5z/ulm3muzRi4/EHXIG3h7e5Dwx6jGt4je5NLpC/+U/Nl6D3HgQmADf92W4El1ku6uw9zH3Bu\ne8d1MT/nB6f95bUXT5Jur63faqu1W1BrTcQrz3IsqDtd4rqTnj7Na/f3F1qP5DmNlec0Vp7ROHlO\nYyVnqpCgAH5z+XC+9uQyHv9kN3efO8D9EDnoIvj8H1BeAkFNVNpVz1zXnukGN9sdHu9mBPtW/ewS\n09OVoleUd5iZHOnk9i4BIDd2RM2x2F7QY7TrID31+20fQ+4BWPIgDL4UjmyG/9wFd37m1pafyQ6t\nd+XbF/0eUief2j02veZ2PxhzoystX/+yG4/Y+gXKzVj9HHzxmPs+NPAi6DrQzXL3HOtmtRsTHAaz\nXoB/ngsv3wrfXgEB9Yqp170EAUEw6qvNv39oFCSOcKXo8OUe3V+qnvn+7GH32P88z782H/OkvLxj\nrd86soXdJpXuaqImIiIibWDaoG5cPDyRhxfv4GBOkTvYa7ybgTnUTCV99Ux3dJJ7rJ105x2CskJI\n6Fd1TQ+wFVDQcbc5l05m7xLoMoDS0Pi6xwdf6raByj/S9jEs/h3YSpjxJ7jyUcjZD+//uu3f93Qc\nWg//usLNBL/1Q9cM7VSseQG6DHTfi5KrdlJoTYl5WTF88Fv3fWrZPHjmCvh/w9ys+VX/aP7Dv+hE\nuPC3bonMrsV1z1VWug8ABlwAkR7sxpAy0S3DAeg+pO65roPcvtz7PnPfK+P7eP71+ViLSXeHWr9V\nUQ5Ht7GloieJ2qNbRERE2sgvLh1GpbX8+e0t7kCv8e7xwMqmX5R3yD1GV5WXx9faq/v4Lvf76qQ7\npmp2qjpRlzPHgVWw6Mft3xzsTFZZAXs/g95nNzw3eCZgYdt/2zaGA6tg7Ysw+U73b6v3WXDW3bD8\ncdj1Udu+d31Hd8Cnf3ez19X/7hsRmb/bJdzB4a4pWfYGWPN869/v2E6XiI65wVXeJI2AwJDWNVNb\n8zzkZ7tO5D/eDbNehLSvw5WPuAZmLRl6OUR0gZVP1z2+d4lr7jbyOs/iSK5a1x3XG0Kj654LDqtZ\ngtOBZrnBs5lurLWLrLWDrLX9rbV/qDo2t/YaLmvtfdbaYdbaEdba+9sq4GYd3wUVpawp6UmS9ugW\nERGRNpKSEMHt0/rx+tqDLNt93CXJUUnNzyzlHYSIrjVNiMLiIDSmiaS7qgRdHczPPCufhmWPwYnd\nvo7kzJG9AUpyG9+iKmkkxCS7EvO2Yi28+wv37+ucH9YcP+8X0GUA/OfbriN2W7IWlj4Mj0yGh8e7\nRm6rnoGnL238w7ND6xmz5pcu4b71TVe+nTwRFv/eNYNrjbXz3Vro0bPc86BQ12m8ftJdXgrzb3Rr\npmurKIclD7gPD/tOc2XeQy6By/5fyyXh1YJCXWn7lkV1P2hY9xKERMHgSzy7T3Uztfrrx6tVfwDQ\n71zP7neG8Cjp7jCOuIr2rZXJKi8XERGRNnVn+gB6xobxm9c3UmFxP7A2N9N9MqumiRq4GanqDubH\nd7o1j7FVbXQ0033m2r/MPR5a79s4ziR73HruRme6jXGz3TsXu72aPfXxfZ4n6lvedDOq5/0cwmJq\njgeHu9LovCyYe46bjW8r+z6Hd3/uEtYZf4bvrYdb34K8bHhqZs32gCX5LrF+/EIqAkNcwp3Qz43T\nxX90s81LHvD8fSsr3Qx/v3NrPqwDV2KetcYl1NW2vOHGauGdkLWu5vjGhZCzF6b+oOX9tpsz/la3\nLGb1c+55WbHraj70cgiJ8Owe8X0g9SwYdHHj55MnQHBkh2uQ519J92FX4rXDqrxcRERE2lZ4SCA/\nvWQom7JO8tLy/dBrHBzb0fR+3XkHa5qoVfsy6d7lftisXjcZkQCBoS0n3fu+UPLXnopzXVdsqJu0\ndHZ7l7i/v0017Ro80zX58rTMe/t7LjF971ctl/GfPOjKuLsPg7GN7FqcMhG+8S4EBrtZ54/vO/V1\n081Z87xLBm9+zZW4x6W6pmhfew0KT8BTl8Bnj8BD41wMg2eyeuxfaqpbAFImwPBrYOlDrgmatW4X\ngxdnw9OXuRnt6g8urHUfSjx+nuvkPfamuvEkp7k+EYc31Rxb9jjEproy8AVfc3+frXWl8F0Hez4b\n3ZQu/d1M+ap/uQ8Dtr/jKiA8nS0Hl/R//b8ugW/MpDvgu6sgLPb0Ym1n/pV0H9lMcVQKRYQRGx7s\n62hERETEz102qgcT+ybwf+9uJb/bWHfwwKrGL64/0w01SfexXXV/+DbGzVq1lHQvnOPWF0v7yFwB\nWNfM6dAZlHRbW7XNUkH7v3dlpUu6G5vlrtZnKoREu63DWlJWDIvugYBgt793U/+ewO0W8NLNLhH9\nylNNN/vqNQ5u/xiGX+WS+eevc3s9e0tpoeu4Pfyqhp3Sk9PgltfdFljv/MytVf7G+3DdU5SEdWt4\nrwt+45rBvfotmDsVnrnSVVecPOi25frbENdw7bFp8OIs9yHfFQ/D8Kvrfc1VfSYyl7vH7I2wbylM\n/CZc95RL1F+7y22/dXgjTP1ew67jp2L8re572q7FsG4BRCVC3+mnf99qgcE1zSg7EP9Kug9v4WT0\nAAAiQ7W9hoiIiLQtYwy/vnwYOYWlPLSl6oftxpKE8hIoPFrTRK1a9V7dR7bUTbqh5aS78Dic2OOS\nv8rK1ge/4iniTqxt/es6s8zlQFW59Jk0073zA3jl67Dq2fZ/7yObXeLXXNIdFAoDL3DN1Fr6u7r0\nQbde/itPQFAYrH2h6WsX3eP6KFz1aMNO1/WFxcC1T8DM+9x4taaEuyVb3nT/jsfc0Pj5nmPgmx/A\nDS+7WfeUCU3fK743nHWX+yDDWpdQf38jfGcl3PKGayC28l8uib/qH26LrnE3NywLj+/j1rhXL3lZ\n9k83nmNvdjPwF/62qtR8jlvW4mmjs5YMudy976f3u4R+xFcgINA79+7A/CbpNpXlcGwHOZHuPywl\n3SIiItIehveM5abJvZm37ChFsf0bX9edn+0eG0u6ASrLIKF/3XMxPZtvpJa1xj2W5rs14a1RfBIW\n3cPA7fPUhbs19n/hGjz1ngL5hyD/DNnSbdUz7nHPJ+3/3tXrufs0k3SDK13Oz26+78GJPfDJ32DY\nVTDsShhyqZvBLy9peO3Kp10Z89Tvu2s9YQxMmuPu/9Ffvlya2qTik24temPvX9ua590MduqUpq/p\n0h8GXeTZmulzfwF3fAp3LnEJdXCYe13faW6W+qf7XbI95gY389sYY9wse+YKV0a+bgGMuNYtXQGY\nfJdba12cC1O+0/R9WisoxMW15xP3fa01peV+zG+S7vCig1BZxuEI9x9WZKg+UREREZH28eMZQ0iO\nDycjPxWbuaJhInsyyz3GNLKmu1pjM915WU3PDB5cXev3a1oX8M4PoLKMyMLM1m0r1JnZSshc6Ro5\nJY10x9q7xLyxD0jyj7iO0SbQzY6eStXD6di7xHUnj+vd/HUDL3Ql5q9/GwqONn7N2z9xX8fFf3TP\nR9/gZtG3vVP3usyVbpa7/3lw3i9bH/Ml90FIpIulsfXd+Yfh/f+Fv4+AZ6+GRya6hmCNjX/OfrdW\nffRs75RngyuTTxrZdIIeHO7Z7HGvNDi6Fb54DMoKYMI3a84Z42bKr3wUxt/mnbirVa/H7joYeoz2\n7r07KL9JuiML9gOQFdIHgCjNdIuIiEg7iQoN4m/XjWFpcR9M4RG3XrK2vKoy8aZmugES+tY9F9ML\nKkqh8Fjjb3pwtXt9YGjNrLentr4N4fFUBITCah+UJHdAEYWZrilUysSapLs9S8wX/x7mpTfsAL5u\nvptRPPu7LkGt3Tir2u6PYf0rnr1PwVHXoM8T1rqku8/ZLc/ghsfD7BfdbPYzV9Xdwsta1/F629uQ\n/pOahmz90t2a4LXza64tzoVXbnPHr33i1EqXo7rDjL+45QJfPFZz/MhWeON7Ltn+9O/QPx2umgtB\n4bDgZvjX5Q0bF66bD9ia7brOJMlp7vHj/3MJeK9xdc+HRsPYG2u2MfSWLv0h/Wdw/q9Orxu6H/Gj\npHsvmAAOBiZjDIQHa6ZbRERE2s/Evgn0GT0NgI0rPqx7sqmZ7uq9ugOCGs4UtrRX98G1btY1cThk\ntWJtdkW5mzkcNJPD3c+GDa/6pgFXBxObW1WKnDzRJZBxqe03052XDUsedB+ufHp/zXFr3Tru5ImQ\n9g13bM+ndV9rLbz1I3jtzuZ7BBSdgA9+C/ePgicvcvte158FzloHT850e1E/fqFLQguONL+eu7a+\n58CsF9zs63PXuAR610fwxEXwn7uh51jX+btaYJArT97+jvswwFqXFOdmuoS7ulT6VIz6Kgy8GBb/\nDta8UDOjveZ5GH29K9/+6jMwZrYr9b70b64Z2bx0VwJfWeHiWfMi9J7a8EOzM0GvcYCBihKY+K32\nfe/0e2HoZe37nmcwP0q690F8H3LKg4gMCcLoUxURERFpZzdeMZNSglmz9H1yC2t1R8476Gakw+Pr\nvqB6r+641Iadl6uT7ryshm9UcBRy97kkpecYl3R7Wla8/3MozoHBMzmUdIFrALXpP55/kZ1UzMkt\nEJ7gZvEAkka133Ztnz/qZrP7nONmYI/vcsf3L3MJ7LivQVyKa55Vf1334c3umopSl7jXV1HutrC6\nfzR88v9g8AxXHrz0IZh/g1vXXFnpXvv4+e69uw5w+y6XFboZ1EEzPP9aBpzvktlD6+GBMfDMFS6J\nvux++MZ7DdcWj54NleVubffqZ2Hjq3DuzyB1UmtGsCFj4LK/uw+8XrsTsje5tdTf3wRXPOS+xmqB\nQa40+zsr3TroD37ruopvXOj6KTTVQM3XwmKh6yC3Rdiwq3wdTafmNzXYEYX7IWU0hSUVWs8tIiIi\nPhEWFk5h95EMPLSNe15Zy9ybxhMQYNxMd3RS46WWY25wCVF90c3MdFev4e451s2Ur3gScvY0XBfe\nmK1vQ2AI9D+P3Ozl7jWrnztzE4czRMzJra60vPrPMGkUbHkLSvIbbhPlTcW57s932FVurfPDE9w2\ncTe+7BqohUTVbBfVeypsfcslydXrizcuBBMAAy50zcfO+YErr6625H5Xuj7kMpfMJg53xxNHwNv3\nwpMXQ2RXV6I+5DK4/EGI7HJ6X9PgmW6Lrw//CNPugbSvu2ZhjUkc7sr5P/+Ha8TWd5prnuYNsb3c\nOOZlweBLWy6zjkhwcQ+4wP0Z7PnE7c3taSM3X7jkPrAVTY+vtAv/mOkuLyGi8CB0H0J+abk6l4uI\niIjPRPSdxNigPXyw6SD3f7DdHcw71LC0vNpZdzeeRER1d02lGisJrm6iljSqplGRJ83UrHWJYt/p\nLlE0Bsbe5NblHmtlB/T2lncI/tK3plN3eyo87prOJdfa6qnHKMBC9oaWX19aALsyXJL56u3w5vdd\n+XbGn2Hv0uZfu/xxKDnp9lGO6QHn/hR2vOfWOW98FUZcU5P095lad123tS7p7jPVJezlxfDZIzX3\nPrTexTD8Gpj1fE3CDa4c+aZ/uw99Mle4ZPv6504/4a427Aq4+3O3PVZLCeHoG9w2YsERcPU8725B\nlTrZfWjh6brm6n8zd3ziKg8m39G2H7qcrn7TXcM58Sn/SLqP7cBQCd2GUlBSriZqIiIi4ju9xhNc\nWcxdw8t48IPtvL0+y5WX12+i1pKAQPeaxpLurDXQZaDbe7j7MAgI9mxd95GtLnkZPLPm2OjZbiZ0\nzfOti+9UFB6H+TfCjg9a/9pP/w5Fx11X6ZK8huePbIPt751+jI2p7vCeMrHmWNIo99hcifmJPW4N\n9J9TXTnyx/e5Dzg2vwHLn4CMP8FTM9165qITDV9fVuRmePufX/PhysTboftw95qyQhh3S8311dt2\nVa/rzt4Ix7a7pLLrAJegL3/c/TmUl8LCO92Sh0v/1nj8/c+Fu75wZdXjb/FdU6xR17sE99rH3QcP\nZ4Iu/eHWN12zMJEW+EfSfXize+zuku7IECXdIiIi4iNVHYL/p/8hxqbG8YMFa6jMPdj0THdzmtqr\n++BqV1oOboYucZhnHcy3LnKPtZPumJ6uXHbNC41vn+QtZcVujfCWN93samuczIIVT0HKJCg8Cksf\nrnu+6AQ8exW88FW3NtfbMpdhCYCetbo/x/R0a7yb+rCjssLNamdvgCnfhRtfgXv3wPc3wD074OdZ\n8PNDrsphzYvw8ETY+FrdbalWP+calZ3zg5pjgUEuSbYV7gOXXuNrzsWluoZ81eu6N77qPlAZeoV7\nfs4P3b7uXzzmPgDIXg+XP9B8Q7KYHqf2d9ebIru4BLf/ub6NQ+QU+UfSfWSL+0bYdSD5WtMtIiIi\nvpTQDxL6EfTuz3gp6DfcFrKYgIpi8kO7tf5eMT0bznTnZbtEvOeYmmM9qpqpNbaPcG1b33bJev0k\nauxNbl3rqcxAe6KyAl79Fuz7zM3aZi5zs+6e+vTvLsm8+jG3fnbpQ24v5WqL7nHl5yFR8M5PG45D\nRZkrSy/OPbX49y8jP6pP3TJiY1yJeVMdzJc+5JrWXXIfXPBrt091WGzda4LD4YLfwJwP3Zr/l29x\n21UtvNMl4ksfdCXt9buD9z7LNQGb+deGs899zqnZr3vjQrcGOrKrO5c43K3L/uwR14F79GwYcsmp\njYmIeMw/ku7DmykK7wFBoRRqTbeIiIj4kjHwjffhwt8RUnyMH1fMA+Dh5fnkFpW18OJ6qpPu2klk\nVq0matV6jHazvTn7mr5X/mG3L/HgRpKsQTPdrO3aFxt/bWlh0wnr0e3w4DjX3bqxDurWwn9/Aptf\nh4v/BFfPdR2jVz/XdKy1nTzoGoCNnu22ZTr/125t8kd/dec3vArrX4bp98J5v3Brp7e+Xfceb98L\nr3+n5jWtUVkBB1ZyMmZww3NJo1zFZUW9P9fsjfDhH1yn61HXt/wePUbDtz6EKx6G5PGw7b/w2h3u\nz3Pq9xsv6077utuCq77qdd3r5rtO49VN1qqd80PXsT4qEWa0suJARE6J3yTdBZGpAK68XEm3iIiI\n+FJkFzj7u24t7C1vsnf4nczPGcrXn15OYWm55/eJ6enW7dZOeA+uAUzNmmKomfVubl33tncAW7e0\nvFpQCIy41jVZK8ppeP6Vr7v9ictLGp776K9u26T3fgnPX+tm4qsd3QGLfgTL5sFZ33ZNs6K6uy2m\n1s5vJFnd5GZha3+91bPc037knnfp77a0WvkU7FnimpL1Gu+SybSvQ9fB8O7Pa2Jd9k9Y8YTbNmnV\nM24LrJYUHHXbqC36Mcw9B0rzyY0d0vC6HqNd5/nas/blpbDwdjerfdn9nq+DDgyCcTe77bTu2en2\nhp71YuMfkjSnel33e792jfiGXF73fK9xbkusG16C8LjW3VtETknHT7oryqG8mILIFADy1UhNRERE\nzhTGQN9z6H3dn/nTrCms3neC259dSUm5h2unq8vAa3cWP7gaug2uW+rcfbibPW5qXffJg65pV0J/\ntxVUY8bMhooS2PRa3eOHt8C2t92s6fIn6p47thM2vOIS6ssfgL2fwT+muET88Qvg4fFuu6vxt8GF\nv6t53diboOBw3cZnJfkwfza88zO4f5TbM/roDjfLPeYGtwd1ten3um3PnrnCJb1Xz3NJa2AwzPij\ni/WLx9ys99v3uiT/hgWuC3hjM+ylhbDpdVem/uhZcF9/WPA1l6RHdoXzfsnRrmc1fF3SSPdYXWJe\nUQaLf+eaq13+QE1Zd2sFBLh7D7mk9c3Lqvd9LzjsOlc31m183Nequq+LSHvo+El3YBD8YBN7+syi\nvKKS4rJKIkK0pltERETOLDNH9uAv147ik+1H+c4Lqz1LvJMnun24X7kNTux1xw6udmu4awsOg25D\nG5/pLslzDcaKc+Gr/2o6ies5DroOcjPQtX3+KASFuSZmH99Xbxb6/7nkd8p33ezz7R+5jusf/sFt\nk3Xh7+AHm+Hy+2v2jQa3Z3RUYt0E+L1fuq/x8gfce33wv/DIBLCVcM6P6sYUnegS/cpyuOh3rjP3\nl/e+AAZe7GJdcIv7mq75JySnQepZ8MU/6jaMKy+Ff10OC2528UQnwXm/hK+/Cz/ZB7e8DtN+RGVg\naMMx6zLAbWO1/mX497dcsr70QRhzIwy5tPFxbg99qsrOh1/juxhE5Ev+MyVsAikodd9ANdMtIiIi\nZ6Lr0lIoLK3g169v5ObHl/HYzeOJj2xmf+C4FPjaa/Ds1fD0pW7LpPxDdddzV+sx2q0FtrYmsa4o\nh5dvc2XbNyyomZltjDFu3fQH/+tmihP6uTLrtfPdLHja1+GxabDkAbdNUs4+dy7tGy4JBjcD/63F\nrilbXGrTCX5gEIye5bqQ52W7LtornoQp33HJ+/hbYd8X8Mn/uUZi8b0b3mP6vW7/4dTJDc9d/Ad4\ndLL7wGL2i25rNXB7or90k+ugPuxKd2zx7+DACreeetT1nu/XDG5btx6jYedit/XW4EtqfvnS8Gvc\n/t++TPxF5Esdf6a7luo1UlrTLSIi0jaMMTOMMVuNMTuMMT9p5roJxphyY8xX2jO+juCWKX14aPZY\n1mTmcO0/lrL3WEHzL+g1Hr72utvq6V9VWz81lnT3HOO206rudl5ZAW/fAzvec1tMDbyg5eBGXQ8Y\nWLfAPV/+hCs5n3yXSy5HXgefPere49P73bVnf7fuPYJCXJLcUln0mJvcWu1lj8F/vgPdhsC5v6g5\nnzoJbnwZpv+48dcHBrku3o29T9eBbouu2952zdeqDb7Elal/9oh7vv19NzOd9g23nro1CXe1q+fC\nbf+FH+1wvx92hYvNlwZeAP+zpvmtwESk3fhV0l1QoqRbRESkrRhjAoFHgJnAMGC2MWZYE9f9BXi3\nfSPsOC4f3ZMXvjmJE4WlXP3oUlbuPdH8C3qOgVvecOu4TSAkNbIuu8do97jgZngoDX6f6GaPp34f\n0m7zLLDYXm4d8NoXoawIlv8TBl7kZrDBdQevLIe3fgirn4WxN0JssudfeG3dBrny+U/+5tYfXz3X\nlcl7S/9zoXu95mcBgTDpDtj/hWsat/B2tx7+4j+c+vvE93HJv68TbRE5Y/lV0p1fUl1erjXdIiIi\nbWAisMNau8taWwrMB65s5LrvAP8GDjdyTqqk9Ung1bvOJiYsiFueXMaGAy3sIZ00Er75Ptz0CoRE\nNnJ+lLumrNglm2fdDdc+Aef9qnWBjZ4NJ/bAWz+CgiPuPtXi+8CEb8LWRW4m/ezvte7e9Y29yT1O\nu6fx2fu2MPYmV3b+0s1u3fl1T7n9skVE2ohffSRXPdMdEeJXX5aIiMiZohewv9bzTGBS7QuMMb2A\nq4FzgQntF1rH1LdrJPPnnMW1/1jKrU8t5993nkXvLo0k1NUS+rlfjQkOc9tMna6hl8ObP4A1z7lO\n532n1z0/7R43Ez70srql26dizI2uBHpQI9uYtZXQaBh/Cyx9CC75a80svohIG/Gr7DS/KulWIzUR\nERGfuR+411pbaVpY02uMmQPMAUhMTCQjI8MrAeTn53vtXu3l7hHwxy9K+MrDH/HzyWHEhbZPMWJT\nYzUkYRJJ2YvZHH8+2R991OB88LgHKA+KxHplnKMh2wsfFrRCQOA0YkZ3Jyc3BTz8Gjri3ytf0Dh5\nTmPluY4+Vn6VnaqRmoiISJs6AKTUep5cday2NGB+VcLdFbjEGFNura23+TNYa+cB8wDS0tJsenq6\nV4LMyMjAW/dqT8NG5zB73ufM2xLM/NsnExMW3Obv2eRYDe8BX8xl6IyfMzSoka2y/MJFrbq6o/69\nam8aJ89prDzX0cfKL9d0R2pNt4iISFtYDgw0xvQ1xoQAs4DXa19gre1rre1jre0DvALc1VjCLQ2N\nSYlj7s3j2Zadx1fnfsb+44W+C6bbYLjs7+C3CbeISPvxq6T7y+7lWtMtIiLiddbacuDbwDvAZmCB\ntXajMeYOY8wdvo3OP0wf1I0nb53AgZwirnxkCct2H/d1SCIicpr8Luk2BiJCNNMtIiLSFqy1i6y1\ng6y1/a21f6g6NtdaO7eRa2+11r7S/lF2bNMGdeO1u88mLjyYGx//nPnL9vk6JBEROQ1+lnRXEBkS\nREuNW0RERETOZP27RbHwrrOZ3K8LP3l1Pd/813L2HfNhubmIiJwyP0u6y7WeW0RERPxCbEQwT906\ngZ/OHMLSnce44O8fcf/72yguq/B1aCIi0gp+lXTnl5arc7mIiIj4jaDAAG6f3p/FP0zn4uFJ3P/+\ndmY+8Ak7Duf7OjQREfGQXyXdBSXlaqImIiIificpNoyHZo/l+W9OIq+4jKsfXcKn24/6OiwREfGA\n/yXdKi8XERERP3X2gK68dvfZ9IwN55anlvHc53t9HZKIiLTAz5LuCqJUXi4iIiJ+LDk+glfuPItp\nA7vyi9c28IMFa8jKLfJ1WCIi0gT/Srq1pltEREQ6geiwYB6/ZQJ3n9ufN9dmMf2+DH7/5iaOF5T6\nOjQREanHo6TbGDPDGLPVGLPDGPOTRs6nG2NyjTFrqn79yvuhtsyVlyvpFhEREf8XGGC45+IhLP7R\ndK4c3ZMnl+xm2l8/ZN7HOymrqPR1eCIiUqXFpNsYEwg8AswEhgGzjTHDGrn0E2vtmKpfv/VynB7J\nLyknMkRrukVERKTzSI6P4L7rRvPO96YxqW8Cf1y0hcsf+pSVe0/4OjQREcGzme6JwA5r7S5rbSkw\nH7iybcNqvYpKS3FZpWa6RUREpFMamBjN47ek8djN48ktKuMrc5fy01fXa723iIiPeZJ/o6H1AAAZ\nSUlEQVR09wL213qeWXWsvinGmHXGmLeNMcO9El0rlFS4RzVSExERkc7KGMPFw5N47wfT+cbZfVmw\nYj/T/voh976yjp1HtLe3iIgveCtDXQWkWmvzjTGXAK8BA+tfZIyZA8wBSExMJCMjw0tvD8dOFgCG\nzD07yajY57X7+pv8/Hyvjrs/01h5TmPlGY2T5zRWIqcnKjSIX1w2jFum9OGfn+zipeX7WbByPzNH\nJPG9CwYxKDHa1yGKiHQaniTdB4CUWs+Tq459yVp7stbvFxljHjXGdLXWHq133TxgHkBaWppNT08/\n1bgbeOHNxUARY0cOI31MYxPxApCRkYE3x92faaw8p7HyjMbJcxorEe9ISYjgt1eO4LvnD+SpJbv5\n19K9vL3hEJeN6sn/nN9gfkRERNqAJ0n3cmCgMaYvLtmeBdxQ+wJjTBKQba21xpiJuLL1Y94OtjnF\n5RZQebmIiIhIfV2jQrnn4iF8c2o//vnJLp5euoe31h1kXPdAgnodZUr/LgQEGF+HKSLil1rMUK21\n5caYbwPvAIHAk9bajcaYO6rOzwW+AtxpjCkHioBZ1lrbhnE3UFy1pluN1EREREQaFx8Zwo9nDOEb\nU/sy75NdPLd0Fzc98QV9ukQwe2IqN07urQkMEREv8+i7qrV2EbCo3rG5tX7/MPCwd0NrHc10i4iI\niHimS1QoP505lPEhhyhMGMTzX+zlT29v4YlPd/OzS4Zy5ZieGKOZbxERb/Cke3mHoJluERERkdYJ\nCTRcNbYXL98xhYV3TSEpNozvvbSGrz72GRsO5Po6PBERv+A3GWr1THdkSKCPIxERERHpeMamxvPa\nXWezYMV+/vrOVi576FOG9ojh4uGJzBiRxODEaM1+i4icAv9LujXTLSIiInJKAgIMsyamMnNED15e\nuZ93Nh7igQ+2c//72+nfLZJZE1K5ZlwvukSF+jpUEZEOw28y1KIKMAYiNNMtIiIiclpiI4L55jn9\n+OY5/TicV8x7m7J5ddUB/rBoM399ZwsXDU9i1oQUpvTvSqC6nouINMtvku6ScktkSJDKnkRERES8\nqHt0GDdO6s2Nk3qzLTuP+cv28+9Vmby1LoukmDCuGdeLa8cn079blK9DFRE5I/lN0l1cAZGhmuUW\nERERaSuDEqP51eXDuHfmYD7YfJhXVmby2Me7eDRjJ2m947l+QgqXjupBRIjf/IgpInLa/OY7YnHV\nTLeIiIiItK3QoEAuGdmDS0b24HBeMQtXHeCl5fu555V1/PaNTVw6qgfTBnVjcr8uJESG+DpcERGf\n8psstbhcTdRERETamjFmBvAAEAg8bq39c73zNwL3AgbIA+601q5t90Cl3XSPDuP26f2ZM60fy3Yf\n56Xl+3lj7UHmL98PwJCkaM4f2p3r01JJ7RLh42hFRNqf32SpxRWWBJWXi4iItBljTCDwCHAhkAks\nN8a8bq3dVOuy3cB0a+0JY8xMYB4wqf2jlfZmjGFSvy5M6teFsopK1h/I5bOdx/h0+1H+kbGTRz7c\nydkDujBrQioXDE0kXM1vRaST8J+kuxyiNNMtIiLSliYCO6y1uwCMMfOBK4Evk25r7dJa138OJLdr\nhHJGCA4MYFxqPONS47n73AFk5Rbx8opMXlq+n++8uJqQwADGpsYxdUBXpg7sypiUODXDFRG/5TdZ\nanGFVdMOERGRttUL2F/reSbNz2J/A3i7TSOSDqFHbDjfPX8gd587gM92HuPj7UdYsuMof3tvG397\nbxsDukdx06RUrhmfTExYsK/DFRHxKr/JUovLrdZ0i4iInCGMMefiku6pzVwzB5gDkJiYSEZGhlfe\nOz8/32v38ne+GqspETBlFOQNiWDN4XI+3F/Ib97YxB8XbWJ890B6xwSSHB1AcrQhNsScEbPg+nvl\nGY2T5zRWnuvoY+U3WaorL9faIBERkTZ0AEip9Ty56lgdxphRwOPATGvtsaZuZq2dh1vzTVpamk1P\nT/dKkBkZGXjrXv7uTBiry4FfAuszc3n28z0s3nKEz7JKvjwfFRpEr7hwesaFkRwfwfje8Uwb1K3d\nu6KfCWPVEWicPKex8lxHHyu/SLorKi2llepeLiIi0saWAwONMX1xyfYs4IbaFxhjUoFXgZuttdva\nP0TpqEYmx/LXr4wG4Fh+CVuz89h6KI+9xwo5kFPEwZwiVuw9wbOf78UYGJUcR/qgbpw7pDujesUS\nEOD72XARkcb4RZZaUFoOqJGaiIhIW7LWlhtjvg28g9sy7Elr7UZjzB1V5+cCvwK6AI9WlQSXW2vT\nfBWzdExdokKZEhXKlP5d6xyvrLSsP5BLxtYjZGw7zIOLt/PAB9vpEhnC9EHdmD64GxP+f3v3HiPX\nWd5x/PvMmfvs7P3q9dq7ThbbwSEOmCRtWuqEtCQB1a1UtaFCAqoIVUALVasW6B9V/6jUSqiFSlwU\nUQotFSkNKUQQICXBBFQSHDBpLrbjS2yv7fXuene9u3O/vf1jJs56L/Yx3vXOjn8fycrOOSez7/w0\nHj/PnPO+Z7CdDa2xNRq5iMhiDdGlpvPVplsLqYmIiKwu59zjwOMLtn1+3s8PAg9e63HJ9SEQMG4Z\naOWWgVY+cs8wU+kCPzo8wQ8OjvODQ+M8ur8626G/NcauwTa29TazsS1Gf1uMja0xOpsiOiMuItdc\nQ3SprzXdCc3pFhEREblutCfC7NnZz56d/ZQrjgOjszx3fIp9x6f5ydFJvvmLMxcdH/YC9LVG2dAS\nY7AzztuGu/j1N3TpakkRWVUN8QmTypcBXV4uIiIicr3yAsaO/hZ29LfwvjuHAEjlS5yeznJqujov\nvDo3PMfp6Qzf+r9RvvrTEcJegNu3tHP7UPWy9N6WalPeHAsRDQWIBD08nR0XkavQEF1q5sKZ7oZ4\nOSIiIiKyApoiQbb2Jtnam1y0r1iu8LMT0zx1cJzvHxjjk08sv+5fLOQx3Aqj8ZPcs72HrmRkNYct\nIg2mIbrUVF4LqYmIiIiIfyEvwB1bOrhjSwefuH872UKZ0ZksZ2dyjM7kmMsVyZUq5IplptIFvvOL\nk3z80Rf4hL3Azf0tbOlMsKkjwab2OP2tMbqSEbqbIyQjwbq4r7iI1I+G6FJfW708HtacbhERERG5\ncrGwx5auJrZ0NS25/67mCXq3vYUnXhrj2Vcn2Xd8mseeP0PFXXxcJBigORYiGQmSiARpiYV408YW\n3jrUzls2t9EcDV2DVyMi9aQhmm7N6RYRERGR1WRmbO9rZntfMzAMQKFU4fT5LKPns0yk8ozP5plI\n5ZnNFknlS6TzJSZSeR56+hif3XuUgMGN3U1s6WxisDPBUGeczR0JBtrj9DZHNXdcpEE1RJea1pxu\nEREREbnGwsEAQ50JhjoTlzwuUyix/+R59h2f4oVTM7wyPseTB8coll8/TR7yjA2tMRLhICHPCHoB\n4mGPXZvbufPGDm4ZaCXkBVb7JYnIKmiILjWTL2FUF7kQEREREakn8XCQO2/s5M4bOy9sK1ccZ85n\nOTGZ4eRUhpHpDCNTGXLFMsWyo1SpcC5V4FNPvsI/fR8SYY83b25ja0+SN/QmeUNPkkKpwpHxFEcn\nUoxMZehMRtjSmeCGriaGOhP0t8XUqIvUgYZoulP5MhEPArokR0RERETWAS9gDLTHGWiPX/K46XSB\nZ45N8uMj53j+1Hn+/ZkT5EuVi46JhgJsbIvz7KtTzGSLF7YHDHqbo2xsjzPYEWe4O8lwTxM3djcx\nmy1xaGyWQ2dTnJhM0xoP0dcSo68lypauBDsH2nS5u8gKaYimO50vEQ3qQ0FEREREGktbIsx9N/dx\n3819QPUM+cmpDK+MzRENedzQlWBDS4xAwHDOMZ0pcmwixavn0oxMZzlVO4v+1MEJvvbcqUXPH/KM\ngbY4M9kik+nChe0diTD3bO/hHTt6GOpsYmIuz/hcjslUgfZEmBu7q2fTo7rSVOSyGqLpThVKRPX3\nXUREREQanBewZeeRmxntiTDtiXZ2DbYv2j+dLnB4PMWR8RTJaJBtvUkGOxMXLkHPFcucncnx4pkZ\nnnhpjG+/MMp/Pjey7FjMoL81xsa2GBtaY2xoiTE5WuTA3qMUShUK5TJeIEAi7BGPBEmEPdri4doY\nw7QlwgQDRjBgeAHTrdakYTVE060z3SIiIiIil9aWCHPbUDu3DS1uyAGiIY/BzgSDnQne9aYN5Etl\nfnJ0kslUge7mCF3JCB2JCOdS+QtzyY9NpDlzPsszRycZm8tTrjg4eBCofkFQXnhPtUtIhD02dSTY\n3B5nc0ecaMgjX7tXerFcoac5yqb2OJs64vS1RAmY4WpPHwt5NMeu/B7pqdraUFqQWVZTQ7y7Mvky\n0YZ4JSIiIiIi9SES9Ni9tXvR9q5kpHbrtIuVyhW+99QPufs33kY4GMALGJWKI1cqk86XSedLTGUK\nTKUKTKULTGcKlCqOSsVRqjhmskVOTmU4PD7HUwfHKZQrhIMBIsEAwYAxnSku+p3zRUMBepqj9CSj\nBAKQLVbIFcoUyhViIY9kNEgyGiJgcGYmy6npLOdrz9nZFGGwo3oLt6HOeO2e7QkGOxZfQu+cI5Uv\nMZkqkIgE6UpGriJluR40RKuaypeIeDrTLSIiIiKyVoJegETIiIVfb1IDASMeDhIPV5vTQS59e7XX\nVGpnyOcvlJwrlhmZqq72fnY2B4BhmFWvfB2bzXF2Ns/YbI5KBVpiIXqbI4SDHpl8iblciVPTGSrO\n0d8a49aBNvrbYlSc48S5DMcn0/z4yARf/3n+orGEvQDxiEciXG2dzqXyFy1m15EIs7W2ojzAbK7I\nbLZErlgmEgwQC3vEQh6xsEfYCxAJBQh7HoeOFvj2xPNMpgvMZIsMdSbYOdDKzoFWtvUmCfpceb5S\ncYsWlK5UHFOZAmdnqjn1NEfpSIS18PQaaYimO10o0Rde61GIiIiIiMhKWKo5jIY8hnuSDNea29WS\nzpd49Vz6wq3YUvkymUKJdL6Mc46OpjCdTRE6miLMZIscOjvLobNz/NdzI3gBIxkNkYwGiYc9pjMV\nssUyuUKZbLFMoVQhX6pQqjiCBl1T52hPhGmKBHnq4DiP/Ky62F0wYLTGw7TFQ7TFw7TEQyQjQZLR\nIIlIkKl0gROTGU5MphmdzRGsfbnRVLtMfmIuT6F88Sr3Ic/oTkbZ3pfk1k1tvHlTG8M9TbwyNsf+\nk+fZf3Kasdk8vS1R+ltj9LfGCAcDpPIl0vkSmUKZtniYvpYovS1RupsjNEWCJMJB4hEPw5jLFZnN\nlZjLFS/ch96s+noGOxM0R0OL8i6UKszmigTMCFh1bYJI7QqH16YLOOfIFsrMZIsUyxW6myNEgutn\nUa/GaLrzJaIxfWsjIiIiIiJXJxEJsqO/hR39Lav2O0rlCj96+ofcddddF7Y55xiZyrJ/ZJpDZ+eY\nzhSYTheZyhQYmcowlyuRylf/tMXDbO6Ic8cNHWxsjVGqONL5Eql8mYpz9DRH6WuJ0tMcBahdBZDj\n9HSWF0/P8P0D44vGdENXgv62OCcm0/zvkXOkC+UL+4IBIxbymMuXrup1D3bEeWN/CxtbYxyfTHNk\nPMXxycySc/8DVp2rHwl5zGYKlL733Yv2dyUjbGiJVm911xplQ0uM7uYI2UKZs7M5xmbznEvlKZQq\nFMsVSmVHOBjgKw/eflWv4ZfREE13Kl8iGvR3+YWIiIiIiMhaCnqBRYu+mRmbOqoLxa226XSB/SPT\nHB1PM9zTxM6BVlrjr1867JxjNluiVKmQiAQvnHUulCoXGvjx2TzpQolMvkS6UKZScTTHQjTHgiQj\nIULBAK620l2hVOHweIoXT8/w/Mh5vvviWTZ3xBnubuLeHb10J6M456g4qDh3YQG9TKFMvlRmamyU\nm7feQEssRDBgjM7kOHM+y5mZLIfH53j68ASZeV8SQPWy/65khEjIIxQwgp4RDa1Nz9gQTfd/f/BO\nXv7Fc2s9DBERERERkbrXlghz97Ye7t629H4zoyW++FLwcDDAQHucgfYr/2Lgt974+s9LzUO/lL17\nJ9m9+4Zl9zvnmM1V5/XHwx7dySjhOjop2xBN9/a+ZsYO1U+oIiIiIiIisrSVXtDNzGiJhWiJLf6i\noB746lTN7F4zO2RmR8zsY5c47q1mVjKz31u5IYqIiIiIiIisT5dtus3MAz4D3AfcBLzbzG5a5rh/\nAJ5Y6UGKiIiIiIiIrEd+znTfBhxxzh1zzhWAh4E9Sxz3J8DXgcVL4YmIiIiIiIhch/zM6e4HRuY9\nPgVctM66mfUDvwvcBbx1uScysw8AHwDo6elh7969Vzjc5aVSqRV9vkalnPxTVv4pK3+Uk3/KSkRE\nRBrFSi2k9ingr5xzlYVL38/nnHsIeAhg165dbvfu3Sv062Hv3r2s5PM1KuXkn7LyT1n5o5z8U1b1\ny8zuBT4NeMAXnHN/v2C/1fbfD2SA9znnfn7NByoiIlIn/DTdp4GBeY831rbNtwt4uNZwdwL3m1nJ\nOfeNFRmliIiIrLl567z8JtUr3/aZ2WPOuZfnHXYfMFz7czvwORZcISciInI98TOnex8wbGZDZhYG\nHgAem3+Ac27IOTfonBsEHgE+qIZbRESk4fhZ52UP8G+u6hmg1cz6rvVARURE6sVlz3Q750pm9mHg\ne1QvJfuic+4lM/vj2v7Pr/IYRUREpD5cdp2XZY7pB0YXPtlqrfWiNQH8U1b+KSt/lJN/ysq/9Z6V\nrzndzrnHgccXbFuy2XbOve/qhyUiIiKNbrXWetGaAP4pK/+UlT/KyT9l5d96z8rP5eUiIiIi4G+d\nFz/HiIiIXDfMObc2v9hsAjixgk/ZCZxbwedrVMrJP2Xln7LyRzn5V69ZbXbOda31INaKmQWBV4C3\nU22k9wF/6Jx7ad4x7wQ+THX18tuBf3bO3ebjuVeyLqjX9089Ulb+KSt/lJN/ysq/es3KV12wUrcM\nu2IrXbSY2XPOuV0r+ZyNSDn5p6z8U1b+KCf/lFV98rnOy+NUG+4jVG8Z9n6fz71idYHeP/4pK/+U\nlT/KyT9l5d96z2rNmm4RERFZfy63zourXkL3oWs9LhERkXqlOd0iIiIiIiIiq6SRmu6H1noA64Ry\n8k9Z+aes/FFO/ikruRp6//inrPxTVv4oJ/+UlX/rOqs1W0hNREREREREpNE10pluERERERERkbqy\n7ptuM7vXzA6Z2REz+9haj6eemNmAmf3AzF42s5fM7CO17e1m9j9mdrj237a1Hms9MDPPzPab2bdq\nj5XTEsys1cweMbODZnbAzH5FWS3NzP6s9nfvRTP7qplFlVWVmX3RzMbN7MV525bNxsw+XvucP2Rm\n71ibUct6oLpgaaoJrpzqAn9UF/ijmmB510NNsK6bbjPzgM8A9wE3Ae82s5vWdlR1pQT8uXPuJuAO\n4EO1fD4GPOmcGwaerD0W+AhwYN5j5bS0TwPfdc5tA26hmpmyWsDM+oE/BXY553ZQvb3SAyir13wJ\nuHfBtiWzqX1uPQC8sfb/fLb2+S9yEdUFl6Sa4MqpLvBHdcFlqCa4rC/R4DXBum66gduAI865Y865\nAvAwsGeNx1Q3nHOjzrmf136eo/oh2E81oy/XDvsy8DtrM8L6YWYbgXcCX5i3WTktYGYtwNuAfwFw\nzhWcc+dRVssJAjEzCwJx4AzKCgDn3NPA1ILNy2WzB3jYOZd3zr1K9f7Pt12Tgcp6o7pgGaoJrozq\nAn9UF1wR1QTLuB5qgvXedPcDI/Men6ptkwXMbBC4FXgW6HHOjdZ2nQV61mhY9eRTwF8ClXnblNNi\nQ8AE8K+1S+6+YGYJlNUizrnTwCeBk8AoMOOcewJldSnLZaPPevFL7xUfVBP4orrAH9UFPqgm+KU0\nVE2w3ptu8cHMmoCvAx91zs3O3+eqy9df10vYm9m7gHHn3M+WO0Y5XRAE3gx8zjl3K5BmwaVQyqqq\nNvdoD9WCZAOQMLP3zD9GWS1P2YisDtUEl6e64IqoLvBBNcHVaYRs1nvTfRoYmPd4Y22b1JhZiOo/\nrv/hnHu0tnnMzPpq+/uA8bUaX524E/htMztO9VLEu83sKyinpZwCTjnnnq09foTqP7bKarF7gFed\ncxPOuSLwKPCrKKtLWS4bfdaLX3qvXIJqAt9UF/inusAf1QRXrqFqgvXedO8Dhs1syMzCVCfVP7bG\nY6obZmZU59gccM7947xdjwHvrf38XuCb13ps9cQ593Hn3Ebn3CDV99BTzrn3oJwWcc6dBUbMbGtt\n09uBl1FWSzkJ3GFm8drfxbdTnUOprJa3XDaPAQ+YWcTMhoBh4KdrMD6pf6oLlqGawD/VBf6pLvBN\nNcGVa6iawKpn69cvM7uf6rwbD/iic+7v1nhIdcPMfg34EfACr89J+gTVOVxfAzYBJ4Dfd84tXLzg\numRmu4G/cM69y8w6UE6LmNlOqgvLhIFjwPupfoGnrBYws78F/oDqqsH7gQeBJpQVZvZVYDfQCYwB\nfwN8g2WyMbO/Bv6IapYfdc59Zw2GLeuA6oKlqSb45aguuDzVBf6oJlje9VATrPumW0RERERERKRe\nrffLy0VERERERETqlppuERERERERkVWipltERERERERklajpFhEREREREVklarpFREREREREVoma\nbhEREREREZFVoqZbREREREREZJWo6RYRERERERFZJf8P7x2/Ro1cPScAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x206b24ec588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epoch = 200\n",
    "batch = 64\n",
    "\n",
    "h = train(model1, batch, epoch)\n",
    "accuracy_curve(h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析\n",
    "\n",
    "- 根据上面的 accuracy 曲线，我们可以看到模型在测试集上所能达到的最高准确度为90%，这一准确度在第85代训练达到。\n",
    "\n",
    "- 但是根据上面的 loss 曲线，可以观察到模型在第40代训练之后 `loss` 不断上升，这说明模型已经进入过拟合状态，不应再继续训练。\n",
    "\n",
    "- 结合上面的分析，batch size为64的模型实际收敛准确度应该是 88%-89%。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练模型：Batch Size = 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_5 (InputLayer)         (None, 32, 32, 3)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_25 (Conv2D)           (None, 32, 32, 64)        1792      \n",
      "_________________________________________________________________\n",
      "conv2d_26 (Conv2D)           (None, 32, 32, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_13 (MaxPooling (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_27 (Conv2D)           (None, 16, 16, 128)       73856     \n",
      "_________________________________________________________________\n",
      "conv2d_28 (Conv2D)           (None, 16, 16, 128)       147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_14 (MaxPooling (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_29 (Conv2D)           (None, 8, 8, 256)         295168    \n",
      "_________________________________________________________________\n",
      "conv2d_30 (Conv2D)           (None, 8, 8, 256)         590080    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_15 (MaxPooling (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten_5 (Flatten)          (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 10)                40970     \n",
      "=================================================================\n",
      "Total params: 1,186,378\n",
      "Trainable params: 1,186,378\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model2 = create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "391/390 [==============================] - 12s - loss: 1.7231 - acc: 0.3762 - val_loss: 1.4053 - val_acc: 0.4891\n",
      "Epoch 2/200\n",
      "391/390 [==============================] - 11s - loss: 1.3003 - acc: 0.5352 - val_loss: 1.0433 - val_acc: 0.6291\n",
      "Epoch 3/200\n",
      "391/390 [==============================] - 11s - loss: 1.0774 - acc: 0.6208 - val_loss: 0.9539 - val_acc: 0.6599\n",
      "Epoch 4/200\n",
      "391/390 [==============================] - 12s - loss: 0.9320 - acc: 0.6747 - val_loss: 0.8397 - val_acc: 0.7176\n",
      "Epoch 5/200\n",
      "391/390 [==============================] - 11s - loss: 0.8276 - acc: 0.7118 - val_loss: 0.7632 - val_acc: 0.7322\n",
      "Epoch 6/200\n",
      "391/390 [==============================] - 11s - loss: 0.7534 - acc: 0.7385 - val_loss: 0.6606 - val_acc: 0.7683\n",
      "Epoch 7/200\n",
      "391/390 [==============================] - 11s - loss: 0.6962 - acc: 0.7587 - val_loss: 0.6774 - val_acc: 0.7700\n",
      "Epoch 8/200\n",
      "391/390 [==============================] - 11s - loss: 0.6464 - acc: 0.7753 - val_loss: 0.6291 - val_acc: 0.7836\n",
      "Epoch 9/200\n",
      "391/390 [==============================] - 11s - loss: 0.6023 - acc: 0.7905 - val_loss: 0.5450 - val_acc: 0.8172\n",
      "Epoch 10/200\n",
      "391/390 [==============================] - 11s - loss: 0.5704 - acc: 0.8028 - val_loss: 0.5420 - val_acc: 0.8127\n",
      "Epoch 11/200\n",
      "391/390 [==============================] - 11s - loss: 0.5414 - acc: 0.8137 - val_loss: 0.4942 - val_acc: 0.8295\n",
      "Epoch 12/200\n",
      "391/390 [==============================] - 11s - loss: 0.5081 - acc: 0.8234 - val_loss: 0.4909 - val_acc: 0.8374\n",
      "Epoch 13/200\n",
      "391/390 [==============================] - 11s - loss: 0.4800 - acc: 0.8341 - val_loss: 0.4722 - val_acc: 0.8406\n",
      "Epoch 14/200\n",
      "391/390 [==============================] - 11s - loss: 0.4580 - acc: 0.8411 - val_loss: 0.4913 - val_acc: 0.8327\n",
      "Epoch 15/200\n",
      "391/390 [==============================] - 11s - loss: 0.4411 - acc: 0.8485 - val_loss: 0.4728 - val_acc: 0.8427\n",
      "Epoch 16/200\n",
      "391/390 [==============================] - 11s - loss: 0.4198 - acc: 0.8540 - val_loss: 0.4462 - val_acc: 0.8509\n",
      "Epoch 17/200\n",
      "391/390 [==============================] - 11s - loss: 0.4023 - acc: 0.8619 - val_loss: 0.4252 - val_acc: 0.8589\n",
      "Epoch 18/200\n",
      "391/390 [==============================] - 11s - loss: 0.3814 - acc: 0.8677 - val_loss: 0.4375 - val_acc: 0.8549\n",
      "Epoch 19/200\n",
      "391/390 [==============================] - 11s - loss: 0.3665 - acc: 0.8737 - val_loss: 0.4318 - val_acc: 0.8572\n",
      "Epoch 20/200\n",
      "391/390 [==============================] - 11s - loss: 0.3545 - acc: 0.8774 - val_loss: 0.4061 - val_acc: 0.8663\n",
      "Epoch 21/200\n",
      "391/390 [==============================] - 11s - loss: 0.3451 - acc: 0.8801 - val_loss: 0.4739 - val_acc: 0.8453\n",
      "Epoch 22/200\n",
      "391/390 [==============================] - 11s - loss: 0.3237 - acc: 0.8874 - val_loss: 0.4690 - val_acc: 0.8478\n",
      "Epoch 23/200\n",
      "391/390 [==============================] - 11s - loss: 0.3146 - acc: 0.8911 - val_loss: 0.3829 - val_acc: 0.8771\n",
      "Epoch 24/200\n",
      "391/390 [==============================] - 11s - loss: 0.3018 - acc: 0.8962 - val_loss: 0.3785 - val_acc: 0.8748\n",
      "Epoch 25/200\n",
      "391/390 [==============================] - 11s - loss: 0.2945 - acc: 0.8963 - val_loss: 0.3607 - val_acc: 0.8839\n",
      "Epoch 26/200\n",
      "391/390 [==============================] - 11s - loss: 0.2830 - acc: 0.9023 - val_loss: 0.4021 - val_acc: 0.8716\n",
      "Epoch 27/200\n",
      "391/390 [==============================] - 11s - loss: 0.2721 - acc: 0.9062 - val_loss: 0.4291 - val_acc: 0.8647\n",
      "Epoch 28/200\n",
      "391/390 [==============================] - 11s - loss: 0.2620 - acc: 0.9093 - val_loss: 0.3626 - val_acc: 0.8821\n",
      "Epoch 29/200\n",
      "391/390 [==============================] - 11s - loss: 0.2549 - acc: 0.9118 - val_loss: 0.3732 - val_acc: 0.8811\n",
      "Epoch 30/200\n",
      "391/390 [==============================] - 11s - loss: 0.2494 - acc: 0.9132 - val_loss: 0.3901 - val_acc: 0.8749\n",
      "Epoch 31/200\n",
      "391/390 [==============================] - 11s - loss: 0.2359 - acc: 0.9174 - val_loss: 0.4045 - val_acc: 0.8735\n",
      "Epoch 32/200\n",
      "391/390 [==============================] - 11s - loss: 0.2303 - acc: 0.9178 - val_loss: 0.4326 - val_acc: 0.8667\n",
      "Epoch 33/200\n",
      "391/390 [==============================] - 11s - loss: 0.2227 - acc: 0.9227 - val_loss: 0.3710 - val_acc: 0.8831\n",
      "Epoch 34/200\n",
      "391/390 [==============================] - 11s - loss: 0.2172 - acc: 0.9249 - val_loss: 0.3630 - val_acc: 0.8872\n",
      "Epoch 35/200\n",
      "391/390 [==============================] - 11s - loss: 0.2115 - acc: 0.9260 - val_loss: 0.3513 - val_acc: 0.8874\n",
      "Epoch 36/200\n",
      "391/390 [==============================] - 11s - loss: 0.2060 - acc: 0.9266 - val_loss: 0.3671 - val_acc: 0.8828\n",
      "Epoch 37/200\n",
      "391/390 [==============================] - 11s - loss: 0.2013 - acc: 0.9294 - val_loss: 0.3963 - val_acc: 0.8773\n",
      "Epoch 38/200\n",
      "391/390 [==============================] - 11s - loss: 0.1906 - acc: 0.9329 - val_loss: 0.4025 - val_acc: 0.8800\n",
      "Epoch 39/200\n",
      "391/390 [==============================] - 11s - loss: 0.1864 - acc: 0.9347 - val_loss: 0.3675 - val_acc: 0.8888\n",
      "Epoch 40/200\n",
      "391/390 [==============================] - 11s - loss: 0.1805 - acc: 0.9359 - val_loss: 0.3967 - val_acc: 0.8797\n",
      "Epoch 41/200\n",
      "391/390 [==============================] - 11s - loss: 0.1762 - acc: 0.9368 - val_loss: 0.3739 - val_acc: 0.8890\n",
      "Epoch 42/200\n",
      "391/390 [==============================] - 11s - loss: 0.1718 - acc: 0.9408 - val_loss: 0.3923 - val_acc: 0.8860\n",
      "Epoch 43/200\n",
      "391/390 [==============================] - 11s - loss: 0.1674 - acc: 0.9413 - val_loss: 0.3736 - val_acc: 0.8869\n",
      "Epoch 44/200\n",
      "391/390 [==============================] - 11s - loss: 0.1635 - acc: 0.9424 - val_loss: 0.3683 - val_acc: 0.8913\n",
      "Epoch 45/200\n",
      "391/390 [==============================] - 11s - loss: 0.1606 - acc: 0.9431 - val_loss: 0.3648 - val_acc: 0.8945\n",
      "Epoch 46/200\n",
      "391/390 [==============================] - 11s - loss: 0.1546 - acc: 0.9464 - val_loss: 0.3799 - val_acc: 0.8897\n",
      "Epoch 47/200\n",
      "391/390 [==============================] - 11s - loss: 0.1481 - acc: 0.9481 - val_loss: 0.3989 - val_acc: 0.8863\n",
      "Epoch 48/200\n",
      "391/390 [==============================] - 11s - loss: 0.1496 - acc: 0.9483 - val_loss: 0.3725 - val_acc: 0.8929\n",
      "Epoch 49/200\n",
      "391/390 [==============================] - 11s - loss: 0.1438 - acc: 0.9501 - val_loss: 0.3803 - val_acc: 0.8930\n",
      "Epoch 50/200\n",
      "391/390 [==============================] - 11s - loss: 0.1428 - acc: 0.9489 - val_loss: 0.4107 - val_acc: 0.8833\n",
      "Epoch 51/200\n",
      "391/390 [==============================] - 11s - loss: 0.1391 - acc: 0.9512 - val_loss: 0.4099 - val_acc: 0.8864\n",
      "Epoch 52/200\n",
      "391/390 [==============================] - 11s - loss: 0.1326 - acc: 0.9518 - val_loss: 0.3797 - val_acc: 0.8959\n",
      "Epoch 53/200\n",
      "391/390 [==============================] - 11s - loss: 0.1331 - acc: 0.9523 - val_loss: 0.4444 - val_acc: 0.8902\n",
      "Epoch 54/200\n",
      "391/390 [==============================] - 11s - loss: 0.1274 - acc: 0.9540 - val_loss: 0.3953 - val_acc: 0.8930\n",
      "Epoch 55/200\n",
      "391/390 [==============================] - 11s - loss: 0.1221 - acc: 0.9578 - val_loss: 0.4449 - val_acc: 0.8831\n",
      "Epoch 56/200\n",
      "391/390 [==============================] - 11s - loss: 0.1205 - acc: 0.9578 - val_loss: 0.3849 - val_acc: 0.8923\n",
      "Epoch 57/200\n",
      "391/390 [==============================] - 11s - loss: 0.1199 - acc: 0.9582 - val_loss: 0.4020 - val_acc: 0.8955\n",
      "Epoch 58/200\n",
      "391/390 [==============================] - 11s - loss: 0.1168 - acc: 0.9585 - val_loss: 0.3802 - val_acc: 0.8961\n",
      "Epoch 59/200\n",
      "391/390 [==============================] - 11s - loss: 0.1170 - acc: 0.9590 - val_loss: 0.3975 - val_acc: 0.8976\n",
      "Epoch 60/200\n",
      "391/390 [==============================] - 12s - loss: 0.1112 - acc: 0.9600 - val_loss: 0.3950 - val_acc: 0.8947\n",
      "Epoch 61/200\n",
      "391/390 [==============================] - 12s - loss: 0.1116 - acc: 0.9596 - val_loss: 0.4055 - val_acc: 0.8919\n",
      "Epoch 62/200\n",
      "391/390 [==============================] - 11s - loss: 0.1067 - acc: 0.9639 - val_loss: 0.4030 - val_acc: 0.8967\n",
      "Epoch 63/200\n",
      "391/390 [==============================] - 11s - loss: 0.1077 - acc: 0.9627 - val_loss: 0.4055 - val_acc: 0.8970\n",
      "Epoch 64/200\n",
      "391/390 [==============================] - 11s - loss: 0.1064 - acc: 0.9624 - val_loss: 0.4378 - val_acc: 0.8943\n",
      "Epoch 65/200\n",
      "391/390 [==============================] - 11s - loss: 0.1042 - acc: 0.9633 - val_loss: 0.4127 - val_acc: 0.8933\n",
      "Epoch 66/200\n",
      "391/390 [==============================] - 12s - loss: 0.1015 - acc: 0.9645 - val_loss: 0.3937 - val_acc: 0.8971\n",
      "Epoch 67/200\n",
      "391/390 [==============================] - 11s - loss: 0.0983 - acc: 0.9659 - val_loss: 0.4467 - val_acc: 0.8927\n",
      "Epoch 68/200\n",
      "391/390 [==============================] - 11s - loss: 0.0974 - acc: 0.9669 - val_loss: 0.4328 - val_acc: 0.8907\n",
      "Epoch 69/200\n",
      "391/390 [==============================] - 11s - loss: 0.0975 - acc: 0.9664 - val_loss: 0.4747 - val_acc: 0.8885\n",
      "Epoch 70/200\n",
      "391/390 [==============================] - 12s - loss: 0.0922 - acc: 0.9667 - val_loss: 0.4889 - val_acc: 0.8917\n",
      "Epoch 71/200\n",
      "391/390 [==============================] - 11s - loss: 0.0931 - acc: 0.9672 - val_loss: 0.4640 - val_acc: 0.8901\n",
      "Epoch 72/200\n",
      "391/390 [==============================] - 11s - loss: 0.0890 - acc: 0.9684 - val_loss: 0.4408 - val_acc: 0.8951\n",
      "Epoch 73/200\n",
      "391/390 [==============================] - 11s - loss: 0.0890 - acc: 0.9691 - val_loss: 0.4208 - val_acc: 0.8925\n",
      "Epoch 74/200\n",
      "391/390 [==============================] - 11s - loss: 0.0856 - acc: 0.9700 - val_loss: 0.4768 - val_acc: 0.8933\n",
      "Epoch 75/200\n",
      "391/390 [==============================] - 11s - loss: 0.0897 - acc: 0.9685 - val_loss: 0.4281 - val_acc: 0.8959\n",
      "Epoch 76/200\n",
      "391/390 [==============================] - 12s - loss: 0.0840 - acc: 0.9706 - val_loss: 0.4704 - val_acc: 0.8968\n",
      "Epoch 77/200\n",
      "391/390 [==============================] - 11s - loss: 0.0854 - acc: 0.9702 - val_loss: 0.4445 - val_acc: 0.8977\n",
      "Epoch 78/200\n",
      "391/390 [==============================] - 11s - loss: 0.0827 - acc: 0.9719 - val_loss: 0.5267 - val_acc: 0.8761\n",
      "Epoch 79/200\n",
      "391/390 [==============================] - 11s - loss: 0.0829 - acc: 0.9717 - val_loss: 0.4197 - val_acc: 0.8982\n",
      "Epoch 80/200\n",
      "391/390 [==============================] - 11s - loss: 0.0828 - acc: 0.9716 - val_loss: 0.4318 - val_acc: 0.8964\n",
      "Epoch 81/200\n",
      "391/390 [==============================] - 11s - loss: 0.0797 - acc: 0.9718 - val_loss: 0.5432 - val_acc: 0.8812\n",
      "Epoch 82/200\n",
      "391/390 [==============================] - 12s - loss: 0.0802 - acc: 0.9729 - val_loss: 0.5092 - val_acc: 0.8900\n",
      "Epoch 83/200\n",
      "391/390 [==============================] - 11s - loss: 0.0761 - acc: 0.9734 - val_loss: 0.4604 - val_acc: 0.9002\n",
      "Epoch 84/200\n",
      "391/390 [==============================] - 12s - loss: 0.0773 - acc: 0.9732 - val_loss: 0.4473 - val_acc: 0.8967\n",
      "Epoch 85/200\n",
      "391/390 [==============================] - 11s - loss: 0.0737 - acc: 0.9740 - val_loss: 0.4709 - val_acc: 0.8952\n",
      "Epoch 86/200\n",
      "391/390 [==============================] - 12s - loss: 0.0736 - acc: 0.9746 - val_loss: 0.5087 - val_acc: 0.8904\n",
      "Epoch 87/200\n",
      "391/390 [==============================] - 11s - loss: 0.0754 - acc: 0.9741 - val_loss: 0.4753 - val_acc: 0.8916\n",
      "Epoch 88/200\n",
      "391/390 [==============================] - 12s - loss: 0.0737 - acc: 0.9746 - val_loss: 0.4332 - val_acc: 0.8926\n",
      "Epoch 89/200\n",
      "391/390 [==============================] - 11s - loss: 0.0732 - acc: 0.9751 - val_loss: 0.4746 - val_acc: 0.8934\n",
      "Epoch 90/200\n",
      "391/390 [==============================] - 11s - loss: 0.0726 - acc: 0.9754 - val_loss: 0.4737 - val_acc: 0.8966\n",
      "Epoch 91/200\n",
      "391/390 [==============================] - 11s - loss: 0.0699 - acc: 0.9765 - val_loss: 0.5066 - val_acc: 0.8937\n",
      "Epoch 92/200\n",
      "391/390 [==============================] - 11s - loss: 0.0675 - acc: 0.9770 - val_loss: 0.4737 - val_acc: 0.8985\n",
      "Epoch 93/200\n",
      "391/390 [==============================] - 11s - loss: 0.0719 - acc: 0.9754 - val_loss: 0.4869 - val_acc: 0.8962\n",
      "Epoch 94/200\n",
      "391/390 [==============================] - 12s - loss: 0.0659 - acc: 0.9769 - val_loss: 0.4990 - val_acc: 0.8957\n",
      "Epoch 95/200\n",
      "391/390 [==============================] - 12s - loss: 0.0668 - acc: 0.9771 - val_loss: 0.4525 - val_acc: 0.8959\n",
      "Epoch 96/200\n",
      "391/390 [==============================] - 12s - loss: 0.0664 - acc: 0.9767 - val_loss: 0.5073 - val_acc: 0.8930\n",
      "Epoch 97/200\n",
      "391/390 [==============================] - 12s - loss: 0.0676 - acc: 0.9768 - val_loss: 0.4329 - val_acc: 0.8982\n",
      "Epoch 98/200\n",
      "391/390 [==============================] - 12s - loss: 0.0671 - acc: 0.9769 - val_loss: 0.4705 - val_acc: 0.8953\n",
      "Epoch 99/200\n",
      "391/390 [==============================] - 12s - loss: 0.0666 - acc: 0.9771 - val_loss: 0.4993 - val_acc: 0.8942\n",
      "Epoch 100/200\n",
      "391/390 [==============================] - 12s - loss: 0.0656 - acc: 0.9771 - val_loss: 0.4491 - val_acc: 0.8989\n",
      "Epoch 101/200\n",
      "391/390 [==============================] - 12s - loss: 0.0626 - acc: 0.9782 - val_loss: 0.4832 - val_acc: 0.8984\n",
      "Epoch 102/200\n",
      "391/390 [==============================] - 12s - loss: 0.0646 - acc: 0.9776 - val_loss: 0.4645 - val_acc: 0.8997\n",
      "Epoch 103/200\n",
      "391/390 [==============================] - 12s - loss: 0.0607 - acc: 0.9794 - val_loss: 0.5019 - val_acc: 0.8991\n",
      "Epoch 104/200\n",
      "391/390 [==============================] - 12s - loss: 0.0614 - acc: 0.9787 - val_loss: 0.4733 - val_acc: 0.9022\n",
      "Epoch 105/200\n",
      "391/390 [==============================] - 12s - loss: 0.0625 - acc: 0.9787 - val_loss: 0.5090 - val_acc: 0.8976\n",
      "Epoch 106/200\n",
      "391/390 [==============================] - 12s - loss: 0.0597 - acc: 0.9789 - val_loss: 0.4902 - val_acc: 0.9000\n",
      "Epoch 107/200\n",
      "391/390 [==============================] - 12s - loss: 0.0565 - acc: 0.9819 - val_loss: 0.5159 - val_acc: 0.8993\n",
      "Epoch 108/200\n",
      "391/390 [==============================] - 11s - loss: 0.0575 - acc: 0.9800 - val_loss: 0.5052 - val_acc: 0.8998\n",
      "Epoch 109/200\n",
      "391/390 [==============================] - 12s - loss: 0.0561 - acc: 0.9809 - val_loss: 0.4518 - val_acc: 0.8962\n",
      "Epoch 110/200\n",
      "391/390 [==============================] - 11s - loss: 0.0571 - acc: 0.9804 - val_loss: 0.5304 - val_acc: 0.8936\n",
      "Epoch 111/200\n",
      "391/390 [==============================] - 11s - loss: 0.0579 - acc: 0.9800 - val_loss: 0.5529 - val_acc: 0.8963\n",
      "Epoch 112/200\n",
      "391/390 [==============================] - 12s - loss: 0.0599 - acc: 0.9798 - val_loss: 0.4891 - val_acc: 0.8991\n",
      "Epoch 113/200\n",
      "391/390 [==============================] - 12s - loss: 0.0579 - acc: 0.9798 - val_loss: 0.4842 - val_acc: 0.9013\n",
      "Epoch 114/200\n",
      "391/390 [==============================] - 12s - loss: 0.0597 - acc: 0.9795 - val_loss: 0.5127 - val_acc: 0.8961\n",
      "Epoch 115/200\n",
      "391/390 [==============================] - 12s - loss: 0.0516 - acc: 0.9822 - val_loss: 0.5530 - val_acc: 0.8931\n",
      "Epoch 116/200\n",
      "391/390 [==============================] - 12s - loss: 0.0546 - acc: 0.9811 - val_loss: 0.4971 - val_acc: 0.8999\n",
      "Epoch 117/200\n",
      "391/390 [==============================] - 11s - loss: 0.0549 - acc: 0.9806 - val_loss: 0.4950 - val_acc: 0.8993\n",
      "Epoch 118/200\n",
      "391/390 [==============================] - 11s - loss: 0.0543 - acc: 0.9821 - val_loss: 0.5313 - val_acc: 0.8962\n",
      "Epoch 119/200\n",
      "391/390 [==============================] - 12s - loss: 0.0552 - acc: 0.9817 - val_loss: 0.5224 - val_acc: 0.8946\n",
      "Epoch 120/200\n",
      "391/390 [==============================] - 11s - loss: 0.0542 - acc: 0.9811 - val_loss: 0.4811 - val_acc: 0.8961\n",
      "Epoch 121/200\n",
      "391/390 [==============================] - 11s - loss: 0.0533 - acc: 0.9815 - val_loss: 0.6214 - val_acc: 0.8897\n",
      "Epoch 122/200\n",
      "391/390 [==============================] - 11s - loss: 0.0512 - acc: 0.9830 - val_loss: 0.5877 - val_acc: 0.8951\n",
      "Epoch 123/200\n",
      "391/390 [==============================] - 12s - loss: 0.0519 - acc: 0.9824 - val_loss: 0.5375 - val_acc: 0.8952\n",
      "Epoch 124/200\n",
      "391/390 [==============================] - 12s - loss: 0.0498 - acc: 0.9834 - val_loss: 0.5845 - val_acc: 0.8927\n",
      "Epoch 125/200\n",
      "391/390 [==============================] - 12s - loss: 0.0497 - acc: 0.9838 - val_loss: 0.4762 - val_acc: 0.8992\n",
      "\n",
      "@ Total Time Spent: 1487.85 seconds\n",
      "@ Best Training Accuracy: 98.38 % achieved at EP #125.\n",
      "@ Best Testing Accuracy: 90.22 % achieved at EP #104.\n"
     ]
    },
    {
     "data": {
      "image/png": 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/0IBbKdW5BHy2H7fWdCullOosNOhW6iBnjGHBhmKSfW7yM5P3GZCqXjAUZnOx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oAAAU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\nIiJSJyq6RUREREREROrk/wHvJNZ7+o6xRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20b85c83828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epoch = 200\n",
    "batch = 128\n",
    "\n",
    "h = train(model2, batch, epoch)\n",
    "accuracy_curve(h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析\n",
    "\n",
    "- 根据上面的 accuracy 曲线，我们可以看到模型在测试集上所能达到的最高准确度为90.22%，这一准确度在第104代训练达到。\n",
    "\n",
    "- 但是根据上面的 loss 曲线，可以观察到模型在第60代训练之后 `loss` 不断上升，这说明模型已经进入过拟合状态，不应再继续训练。\n",
    "\n",
    "- 结合上面的分析，batch size为128的模型实际收敛准确度应该是 89%。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 总结\n",
    "\n",
    "使用相同的模型和不同的batch size，将模型训练过程用Tensorboard记录绘制如下，可以清晰的看到不同模型在训练过程中的性能差异。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image](https://github.com/mtyylx/Resources/blob/master/CNN%20-%20Batch%20Size%20vs%20Accuracy%20%26%20Loss.png?raw=true)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
